PatchSize
Emanuele Giacomuzzo
2024-10-30 09:53
Design
Ecosystem size is a key factor driving biodiversity and ecosystem function. Larger ecosystems contain more species and can be hubs of dispersal and resource flows in networks of multiple ecosystems. However, whether and how ecosystem size and resource flows interact to affect biodiversity and ecosystem function has been largely overlooked. Here, we investigated how ecosystem size asymmetry affects biodiversity and function of two-ecosystem meta-ecosystems connected through flows of non-living resources. We conducted microcosm experiments, mimicking resource flows between ecosystems of different sizes, yet otherwise being identical. We found that meta-ecosystems with asymmetric ecosystem sizes had higher β-diversity but lower α-diversity and ecosystem function (total biomass) than their unconnected counterparts, while such an effect was not found for meta-ecosystems of identical ecosystem sizes. Our work demonstrates of how cross-ecosystem dynamics modulated by differences in ecosystem sizes affect biodiversity and function, with a direct implication for conservation and management of connected ecosystems.
Parameters
# --- SET UP R MARKDOWN AND GENERAL CODING RUNNING PARAMETERS --- #
set.seed(420)
start_time = Sys.time()
knitr::opts_chunk$set(message = FALSE,
cache = FALSE,
autodep = FALSE)
recompute_lengthy_analyses = FALSE
plot_model_residuals_metaecos = FALSE# --- SET UP PARAMETERS RELATED TO RESOURCE FLOWS --- #
disturbance_levels = c("low", "high")
n_disturbance_levels = length(disturbance_levels)
resource_flow_days = c(5, 9, 13, 17, 21, 25)
first_resource_flow = resource_flow_days[1]# --- SET UP SAMPLING PARAMETERS --- #
total_frames = 125
volume_recorded_μl = 34.4
time_points = 0:7
time_points_without_t0 = 1:7
time_point_names = c("t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7")
sampling_days = c(0, 4, 8, 12, 16, 20, 24, 28)
first_time_point = 0
last_time_point = length(sampling_days) - 1
n_time_points = last_time_point + 1
nr_videos = c(12, 1, 1, 1, 1, 1, 2, 2) #Videos taken for each time point for each culture. At t0 we took 12 videos of the large bottle from which we started the cultures. Write why 2 at the end.
videos_taken = data.frame(time_point = 0 : 7,
nr_videos = c(12, 1, 1, 1, 1, 1, 2, 2))
n_videos_taken_t0 = nr_videos[1]
time_point_day = data.frame(time_point = first_time_point:last_time_point,
day = sampling_days,
video_replicates = nr_videos)
videos_to_take_off = data.frame(culture_ID = NA,
time_point = NA,
file = NA) %>%
add_row(culture_ID = 137-110,
time_point = 7,
file = 137) %>%
slice(-1)
n_cultures = 110
total_number_of_video_rows = sum(nr_videos * n_cultures)# --- SET UP PROTIST PARAMETERS --- #
protist_species = c("Ble", "Cep", "Col", "Eug", "Eup", "Lox", "Pau", "Pca", "Spi", "Spi_te", "Tet")
protist_species_indiv_per_volume = paste0(protist_species, "_indiv_per_volume")
protist_species_indiv_per_ml = paste0(protist_species, "_indiv_per_ml")
protist_species_dominance = paste0(protist_species_indiv_per_ml, "_dominance")
protist_species_total = paste0(protist_species, "_tot_indiv")
n_protist_species = length(protist_species)
first_protist = protist_species[1]
last_protist = protist_species[n_protist_species]
species_IDD_with_13_threshold = c("Col", "Eug", "Eup", "Lox", "Pau", "Pca", "Spi_te", "Tet")
species_IDD_with_13_threshold_indiv_per_volume = paste0(species_IDD_with_13_threshold, "_indiv_per_volume")
species_IDD_with_40_threshold = c("Ble", "Cep", "Spi")
species_IDD_with_40_threshold_indiv_per_volume = paste0(species_IDD_with_40_threshold, "_indiv_per_volume")# --- SET UP ECOSYSTEM PARAMETERS --- #
ecosystems_to_take_off = 60 #Culture ID = 60 as it was spilled (small unconnected, high disturbance, system nr = 40)
ecosystems_info = read.csv(here("1_data", "ecosystems_info.csv"), header = TRUE)
columns_ecosystems = c("time_point",
"day",
"culture_ID",
"system_nr",
"disturbance",
"ecosystem_type",
"connection",
"ecosystem_size",
"ecosystem_size_ml",
"metaecosystem",
"metaecosystem_type")
columns_treatments = columns_ecosystems[!columns_ecosystems %in% c("system_nr", "culture_ID")]
variables_ecosystems = c("bioarea_mm2_per_ml",
"bioarea_tot_mm2",
"indiv_per_ml",
"indiv_tot",
"species_richness",
"shannon",
"simpson",
"inv_simpson",
"evenness_pielou",
"median_body_area_µm2",
paste0(protist_species, "_indiv_per_ml"),
paste0(protist_species, "_tot_indiv"),
paste0(protist_species_indiv_per_ml, "_dominance"))
baseline_columns = paste0("baseline_", variables_ecosystems)
ecosystem_types_ordered = c("Small connected to large",
"Small connected to small",
"Small unconnected",
"Medium connected to medium",
"Medium unconnected",
"Large connected to small",
"Large connected to large",
"Large unconnected")
treatments_and_controls = data.frame(treatment = c("Small connected to small",
"Small connected to large",
"Medium connected to medium",
"Large connected to large",
"Large connected to small"),
control = c("Small unconnected",
"Small unconnected",
"Medium unconnected",
"Large unconnected",
"Large unconnected"))
n_treatments = length(unique(treatments_and_controls$treatment))
n_controls = length(unique(treatments_and_controls$control))
n_replicates = 5
n_ecosystem_types = 8# --- SET UP SIZE CLASSES PARAMETERS --- #
n_size_classes = 12
columns_classes = c(columns_ecosystems,
"size_class_n",
"mean_class_area_µm2")# --- SET UP META-ECOSYSTEM PARAMETERS --- #
metaecosystems_to_take_off = ecosystems_info %>%
filter(culture_ID %in% ecosystems_to_take_off) %>%
pull(system_nr) %>%
unique
system_nr_metaecosystems = ecosystems_info %>%
filter(metaecosystem == "yes") %>%
pull(system_nr) %>%
unique
n_metaecosystems = length(system_nr_metaecosystems)
variables_metaecos = c("total_metaecosystem_bioarea_mm2",
"jaccard_index",
"bray_curtis",
"beta_spatial_turnover",
"beta_nestedness",
"beta_total",
"metaecosystem_richness")
metaecosystem_types_ordered = c("Small-Small meta-ecosystem",
"Medium-Medium meta-ecosystem",
"Medium-Medium unconnected",
"Large-Large meta-ecosystem",
"Small-Large meta-ecosystem",
"Small-Large unconnected")
metaecosystem_type_selected = c("Medium-Medium",
"Small-Large")Name of the axes per response variable.
# --- SET UP AXES LABELS PARAMETERS --- #
axis_names = data.frame(variable = NA,
axis_name= NA) %>%
add_row(variable = "day", axis_name = "Time (day)") %>%
add_row(variable = "ecosystem_size_ml", axis_name = "Patch size (ml)") %>%
add_row(variable = "log_size_class", axis_name = "Log size (μm2)") %>%
add_row(variable = "class_indiv_per_µl", axis_name = "Density (ind/ml)") %>%
add_row(variable = "bioarea_mm2_per_ml", axis_name = "Biomass (mm2/ml)") %>%
add_row(variable = "sqrt_bioarea_mm2_per_ml", axis_name = "Sqrt Biomass (mm2/ml)") %>%
add_row(variable = "bioarea_mm2_per_ml_d", axis_name = "Bioamass ES") %>%
add_row(variable = "bioarea_tot", axis_name = "Total Biomass (mm2)") %>%
add_row(variable = "total_metaecosystem_bioarea_mm2", axis_name = "Total Biomass (mm2)") %>%
add_row(variable = "species_richness", axis_name = "Species Richness") %>%
add_row(variable = "species_richness_d", axis_name = "Species Richness ES") %>%
add_row(variable = "mean_richness", axis_name = "Mean α-Diversity (Shannon)") %>%
add_row(variable = "mean_shannon", axis_name = "Mean α-Diversity (Shannon)") %>%
add_row(variable = "shannon", axis_name = "Biodiversity (Shannon)") %>%
add_row(variable = "sqrt_shannon", axis_name = "Sqrt Biodiversity (Shannon)") %>%
add_row(variable = "shannon_d", axis_name = "Biodiversity ES (Shannon ES)") %>%
add_row(variable = "bray_curtis", axis_name = "β-Diversity (Bray-Curtis)") %>%
add_row(variable = "beta_spatial_turnover", axis_name = "Turn over (Simpson pair-wise dissimilarity)") %>%
add_row(variable = "beta_nestedness", axis_name = "Nestedness (nestedness-fraction of Sorensen)") %>%
add_row(variable = "beta_total", axis_name = "Tot β-Diversity (Sorensen)") %>%
add_row(variable = "metaecosystem_richness", axis_name = "γ-Diversity (Species Richness)") %>%
add_row(variable = "indiv_per_ml", axis_name = "Abundance (ind/ml)") %>%
add_row(variable = "indiv_per_ml_d", axis_name = "Abundance ES") %>%
add_row(variable = "median_body_area_µm2", axis_name = "Median Body Size (µm²)") %>%
add_row(variable = "median_body_area_µm2_d", axis_name = "Median Body Size ES") %>%
add_row(variable = "Ble_indiv_per_ml", axis_name = "Ble Density (ind/ml)") %>%
add_row(variable = "Cep_indiv_per_ml", axis_name = "Cep Density (ind/ml)") %>%
add_row(variable = "Col_indiv_per_ml", axis_name = "Col Density (ind/ml)") %>%
add_row(variable = "Eug_indiv_per_ml", axis_name = "Eug Density (ind/ml)") %>%
add_row(variable = "Eup_indiv_per_ml", axis_name = "Eup Density (ind/ml)") %>%
add_row(variable = "Lox_indiv_per_ml", axis_name = "Lox Density (ind/ml)") %>%
add_row(variable = "Pau_indiv_per_ml", axis_name = "Pau Density (ind/ml)") %>%
add_row(variable = "Pca_indiv_per_ml", axis_name = "Pca Density (ind/ml)") %>%
add_row(variable = "Spi_indiv_per_ml", axis_name = "Spi Density (ind/ml)") %>%
add_row(variable = "Spi_te_indiv_per_ml", axis_name = "Spi te Density (ind/ml)") %>%
add_row(variable = "Tet_indiv_per_ml", axis_name = "Tet Density (ind/ml)") %>%
add_row(variable = "auto_hetero_ratio", axis_name = "Photosynthetisers-Heterotrops Ratio") %>%
add_row(variable = "sqrt_auto_hetero_ratio", axis_name = "Sqrt Photosynthetisers-Heterotrops Ratio") %>%
add_row(variable = "Ble_indiv_per_ml_d", axis_name = "Ble Density ES") %>%
add_row(variable = "Cep_indiv_per_ml_d", axis_name = "Cep Density ES") %>%
add_row(variable = "Col_indiv_per_ml_d", axis_name = "Col Density ES") %>%
add_row(variable = "Eug_indiv_per_ml_d", axis_name = "Eug Density ES") %>%
add_row(variable = "Eup_indiv_per_ml_d", axis_name = "Eup Density ES") %>%
add_row(variable = "Lox_indiv_per_ml_d", axis_name = "Lox Density ES") %>%
add_row(variable = "Pau_indiv_per_ml_d", axis_name = "Pau Density ES") %>%
add_row(variable = "Pca_indiv_per_ml_d", axis_name = "Pca Density ES") %>%
add_row(variable = "Spi_indiv_per_ml_d", axis_name = "Spi Density ES") %>%
add_row(variable = "Spi_te_indiv_per_ml_d", axis_name = "Spi te Density ES") %>%
add_row(variable = "Tet_indiv_per_ml_d", axis_name = "Tet Density ES") %>%
add_row(variable = "Ble_indiv_per_ml_dominance", axis_name = "Ble Dominance (%)") %>%
add_row(variable = "Cep_indiv_per_ml_dominance", axis_name = "Cep Dominance (%)") %>%
add_row(variable = "Col_indiv_per_ml_dominance", axis_name = "Col Dominance (%)") %>%
add_row(variable = "Eug_indiv_per_ml_dominance", axis_name = "Eug Dominance (%)") %>%
add_row(variable = "Eup_indiv_per_ml_dominance", axis_name = "Eup Dominance (%)") %>%
add_row(variable = "Lox_indiv_per_ml_dominance", axis_name = "Lox Dominance (%)") %>%
add_row(variable = "Pau_indiv_per_ml_dominance", axis_name = "Pau Dominance (%)") %>%
add_row(variable = "Pca_indiv_per_ml_dominance", axis_name = "Pca Dominance (%)") %>%
add_row(variable = "Spi_indiv_per_ml_dominance", axis_name = "Spi Dominance (%)") %>%
add_row(variable = "Spi_te_indiv_per_ml_dominance", axis_name = "Spi te Dominance (%)") %>%
add_row(variable = "Tet_indiv_per_ml_dominance", axis_name = "Tet Dominance (%)") %>%
add_row(variable = "Ble_indiv_per_ml_dominance_d", axis_name = "Ble Dominance ES") %>%
add_row(variable = "Cep_indiv_per_ml_dominance_d", axis_name = "Cep Dominance ES") %>%
add_row(variable = "Col_indiv_per_ml_dominance_d", axis_name = "Col Dominance ES") %>%
add_row(variable = "Eug_indiv_per_ml_dominance_d", axis_name = "Eug Dominance ES") %>%
add_row(variable = "Eup_indiv_per_ml_dominance_d", axis_name = "Eup Dominance ES") %>%
add_row(variable = "Lox_indiv_per_ml_dominance_d", axis_name = "Lox Dominance ES") %>%
add_row(variable = "Pau_indiv_per_ml_dominance_d", axis_name = "Pau Dominance ES") %>%
add_row(variable = "Pca_indiv_per_ml_dominance_d", axis_name = "Pca Dominance ES") %>%
add_row(variable = "Sp_indiv_per_mli_dominance_d", axis_name = "Spi Dominance ES") %>%
add_row(variable = "Spi_te_indiv_per_ml_dominance_d", axis_name = "Spi te Dominance ES") %>%
add_row(variable = "Tet_indiv_per_ml_dominance_d", axis_name = "Tet Dominance ES") %>%
add_row(variable = "dominance", axis_name = "Dominance (%)") %>%
add_row(variable = "log_abundance", axis_name = "Log Abundance + 1 (ind/mm²)") %>%
add_row(variable = "abundance_hedges_d", axis_name = "Density ES") %>%
add_row(variable = "beta_diversity_from_unconnected", axis_name = "Divergence from unconnected") %>%
add_row(variable = "beta_diversity_from_previous_time", axis_name = "Temporal Divergence") %>%
add_row(variable = "beta_diversity_from_previous_time_d", axis_name = "Temporal Divergence ES") %>%
add_row(variable = "evenness_pielou", axis_name = "Evenness") %>%
add_row(variable = "evenness_pielou_d", axis_name = "Evenness ES") %>%
slice(-1)# --- SET UP COLOUR AND LINE TYPER PER ECOSYSTEM/META-ECOSYSTEM PARAMETERS --- #
treatment_colours = c("Small" = "#feb24c",
"Medium" = "#1b7837",
"Large" = "#3182bd",
"Small-Small" = "#fc9272",
"Large-Large" = "#67000d",
"Small-Large" = "#762a83",
"Medium-Medium" = "#1b7837",
"symmetric" = "#1b7837",
"asymmetric" = "#762a83")
treatment_colours_paper = c("symmetric" = "#1b7837",
"asymmetric" = "#762a83")
treatment_linetype_paper = c("connected" = "solid",
"unconnected" = "dashed")
treatment_linetype = c("connected to small" = "solid",
"connected to medium" = "dashed",
"connected to large" = "longdash",
"connected" = "solid",
"unconnected" = "dotted")# --- SET UP PLOTTING PARAMETERS --- #
figures_height_rmd_output = 7
legend_position = "top"
legend_width_cm = 2
size_legend = 12
size_x_axis = 13
size_y_axis = size_x_axis
boxplot_width = 2
dodging = 0.5
width_errorbar = 0.2
dodging_error_bar = 0.5
treatment_lines_linewidth = 1
treatment_points_size = 2.5
resource_flow_line_type = "solid"
resource_flow_line_colour = "#d9d9d9"
resource_flow_line_width = 0.3
zero_line_colour = "grey"
zero_line_line_type = "dotted"
zero_line_line_width = 0.5
zero_line_ES_line_type = "dotted"
zero_line_ES_colour = "grey"
zero_line_ES_line_width = 1
ggarrange_margin_top = 0
ggarrange_margin_bottom = 0
ggarrange_margin_left = 0
ggarrange_margin_right = 0
paper_width = 17.3
paper_height = 20
paper_units = "cm"
paper_res = 600
paper_y_axis_size = 9
paper_labels_size = 9
presentation_figure_size = 15
presentation_figure_width = 30
presentation_figure_height = 22
presentation_legend_size = 20
presentation_x_axis_size = 22
presentation_y_axis_size = presentation_x_axis_size
presentation_axes_size = 12
presentation_treatment_points_size = 5
presentation_treatment_linewidth = 2
presentation_figure_units = "cm"
presentation_figure_res = 600
grey_background_xmin = -Inf
grey_background_xmax = 7.5
grey_background_ymin = -Inf
grey_background_ymax = Inf
grey_background_fill = "#f0f0f0"
grey_background_alpha = 0.03
grey_background_color = "transparent"# --- SET UP MODELLING PARAMETERS --- #
time_point_of_baselines = 1
time_points_with_water_addtion = 3:7
time_points_model = 2:7Data
Ecosystems information (ecosystems_info)
# --- IMPORT ECOSYSTEM INFORMATION --- #
ecosystems_info = read.csv(here("1_data", "ecosystems_info.csv"), header = TRUE)Individuals (ds_individuals)
In this dataset (ds_individuals) each row represents an
individual at a time point.
# --- IMPORT T0 --- #
# Import the individual data of t0. We considered cultures to be all the same at the beginning (t0). Because of this reason, we filmed only the bottles from which cultures were assembled. Because we want to plot also t0 for the different treatments, we want to assign the video of bottles to all cultures at t0.
ds_individuals_t0_not_elongated = read.csv(here("1_data",
"individuals_13_threshold",
"t0.csv")) %>%
mutate(time_point = as.numeric(str_extract(time_point, "\\d+")),
day = 0,
file = as.numeric(str_extract(file, "\\d+")),
video_replicate = file) %>%
select(time_point,
day,
video_replicate,
file,
id,
N_frames,
mean_area)
# Elongate t0
ds_individuals_t0_elongated = ds_individuals_t0_not_elongated %>%
map_dfr(.x = 1 : nrow(ecosystems_info),
.f = ~ ds_individuals_t0_not_elongated) %>%
arrange(id) %>% #Id refers to an individual
mutate(culture_ID = rep(1 : nrow(ecosystems_info),
times = nrow(ds_individuals_t0_not_elongated))) %>%
select(time_point,
day,
video_replicate,
file,
culture_ID,
id,
N_frames,
mean_area)
expect_equal(nrow(ds_individuals_t0_not_elongated) * nrow(ecosystems_info),
nrow(ds_individuals_t0_elongated))# --- IMPORT ALL TIME POINTS BUT T0 --- #
# Set up parameters
ds_individuals_t1_to_t7 = NULL
# Import all time points but t0
for (time_point_i in time_points_without_t0) {
ds_individuals_t1_to_t7[[time_point_i]] = read.csv(here("1_data",
"individuals_13_threshold",
paste0("t",
time_point_i,
".csv"))) %>%
mutate(time_point = as.numeric(str_extract(time_point, "\\d+")),
day = time_point_day$day[time_point_day$time_point == time_point_i],
file = as.numeric(str_extract(file, "\\d+")),
video_replicate = ceiling(file/n_cultures)) #Until 110 video replicate = 1, then 2
}
# Tidy up all time points but t0
ds_individuals_t1_to_t7 = ds_individuals_t1_to_t7 %>%
bind_rows() %>%
select(time_point,
day,
video_replicate,
file,
culture_ID,
id,
N_frames,
mean_area)# --- BIND T0 WITH OTHER TIME POINTS --- #
ds_individuals = rbind(ds_individuals_t0_elongated,
ds_individuals_t1_to_t7) %>%
left_join(ecosystems_info,
by = "culture_ID") # --- RENAME AND SELECT COLUMNS --- #
ds_individuals = ds_individuals %>%
rename(ecosystem_size = patch_size,
ecosystem_size_volume = patch_size_volume) %>%
select(disturbance,
disturbance_volume,
time_point,
day,
video_replicate,
culture_ID,
system_nr,
file,
eco_metaeco_type,
ecosystem_size,
ecosystem_size_volume,
metaecosystem,
metaecosystem_type,
mean_area,
N_frames) %>%
rename(ecosystem_size_ml = ecosystem_size_volume,
ecosystem_type = eco_metaeco_type,
body_area_µm2 = mean_area)# --- RENAME AND REORDER LEVELS --- #
ds_individuals <- ds_individuals %>%
mutate(ecosystem_type = case_when(ecosystem_type == "S" ~ "Small unconnected",
ecosystem_type == "M" ~ "Medium unconnected",
ecosystem_type == "L" ~ "Large unconnected",
ecosystem_type == "S (S_S)" ~ "Small connected to small",
ecosystem_type == "S (S_L)" ~ "Small connected to large",
ecosystem_type == "M (M_M)" ~ "Medium connected to medium",
ecosystem_type == "L (S_L)" ~ "Large connected to small",
ecosystem_type == "L (L_L)" ~ "Large connected to large",
TRUE ~ ecosystem_type),
ecosystem_type = factor(ecosystem_type,
levels = ecosystem_types_ordered),
ecosystem_size = case_when(ecosystem_size == "S" ~ "Small",
ecosystem_size == "M" ~ "Medium",
ecosystem_size == "L" ~ "Large",
TRUE ~ ecosystem_type),
ecosystem_size = factor(ecosystem_size,
levels = "Small",
"Medium",
"Large"),
size_connected_ecosystem = case_when(ecosystem_type == "Small connected to small" ~ "Small",
ecosystem_type == "Small connected to large" ~ "Large",
ecosystem_type == "Medium connected to medium" ~ "Medium",
ecosystem_type == "Large connected to large" ~ "Large",
ecosystem_type == "Large connected to small" ~ "Small",
TRUE ~ NA_character_))# --- TAKE OFF PROBLEMATIC VIDEOS --- #
# Set up parameters for testing
ds_individuals_before_taking_off_videos = ds_individuals
# Take off problematic videos
ds_individuals = ds_individuals %>%
filter(!(time_point %in% videos_to_take_off$time_point & file %in% videos_to_take_off$file))
# Test code
diff = setdiff(ds_individuals_before_taking_off_videos, ds_individuals)
expect_equal(nrow(videos_to_take_off),
nrow(expand.grid(diff$culture_ID, diff$time_point, diff$file) %>% unique()))# --- TAKE OFF PROBLEMATIC ECOSYSTEMS --- #
# Set up parameters for testing
ds_individuals_before_taking_off_cultures = ds_individuals
# Take off problematic ecosystems
ds_individuals = ds_individuals %>%
filter(!culture_ID %in% ecosystems_to_take_off)
# Test code
expect_equal(setdiff(ds_individuals_before_taking_off_cultures,
ds_individuals) %>%
pull(culture_ID) %>%
unique(),
ecosystems_to_take_off)Patches (ds_ecosystems)
In this dataset (ds_ecosystems) each row represents a
ecosystem at a time point. I use the data from the 40 threshold analysis
for Ble, Cep, Spi and the data from the 13 threshold analysis for all
the other protists (Col, Eup, Lox, Pau, Pca, Spi te, Tet).
# --- IMPORT & BIND T0 DATASETS --- #
# Import t0
ds_ecosystems_t0 = read.csv(here("1_data",
"ecosystems_13_threshold",
"t0.csv")) %>%
mutate(time_point = as.numeric(str_extract(time_point, "\\d+")),
day = 0,
video_replicate = file) %>%
select(time_point,
day,
video_replicate,
file,
bioarea_per_volume,
indiv_per_volume)
# Describe code
species_ID_13_threshold_t0 = read.csv(here("1_data",
"species_ID_13_threshold",
paste0("t0.csv"))) %>%
rename(Ble_indiv_per_volume = Ble,
Cep_indiv_per_volume = Cep,
Col_indiv_per_volume = Col,
Eug_indiv_per_volume = Eug,
Eup_indiv_per_volume = Eup,
Lox_indiv_per_volume = Lox,
Pau_indiv_per_volume = Pau,
Pca_indiv_per_volume = Pca,
Spi_indiv_per_volume = Spi,
Spi_te_indiv_per_volume = Spi_te,
Tet_indiv_per_volume = Tet) %>%
select(file,
all_of(species_IDD_with_13_threshold_indiv_per_volume))
species_ID_40_threshold_t0 = read.csv(here("1_data",
"species_ID_40_threshold",
paste0("t0.csv"))) %>%
rename(Ble_indiv_per_volume = Ble,
Cep_indiv_per_volume = Cep,
Col_indiv_per_volume = Col,
Eug_indiv_per_volume = Eug,
Eup_indiv_per_volume = Eup,
Lox_indiv_per_volume = Lox,
Pau_indiv_per_volume = Pau,
Pca_indiv_per_volume = Pca,
Spi_indiv_per_volume = Spi,
Spi_te_indiv_per_volume = Spi_te,
Tet_indiv_per_volume = Tet) %>%
select(file,
all_of(species_IDD_with_40_threshold_indiv_per_volume))
# Describe code
ds_ecosystems_t0 = ds_ecosystems_t0 %>%
left_join(species_ID_13_threshold_t0,
by = "file") %>%
left_join(species_ID_40_threshold_t0,
by = "file") %>%
mutate(file = as.numeric(str_extract(file, "\\d+")))# --- ELONGATE T0 DATASET --- #
# Set up parameters
ds_ecosystems_t0_elongated <- list()
# Describe code
for (video_i in 1 : n_videos_taken_t0) {
# Describe code
single_video = ds_ecosystems_t0 %>%
filter(file == video_i)
# Describe code
ds_ecosystems_t0_elongated[[video_i]] = ecosystems_info %>%
mutate(time_point = 0,
day = 0,
file = single_video$file,
video_replicate = single_video$video_replicate,
bioarea_per_volume = single_video$bioarea_per_volume,
indiv_per_volume = single_video$indiv_per_volume,
Ble_indiv_per_volume = single_video$Ble_indiv_per_volume,
Cep_indiv_per_volume = single_video$Cep_indiv_per_volume,
Col_indiv_per_volume = single_video$Col_indiv_per_volume,
Eug_indiv_per_volume = single_video$Eug_indiv_per_volume,
Eup_indiv_per_volume = single_video$Eup_indiv_per_volume,
Lox_indiv_per_volume = single_video$Lox_indiv_per_volume,
Pau_indiv_per_volume = single_video$Pau_indiv_per_volume,
Pca_indiv_per_volume = single_video$Pca_indiv_per_volume,
Spi_indiv_per_volume = single_video$Spi_indiv_per_volume,
Spi_te_indiv_per_volume = single_video$Spi_te_indiv_per_volume,
Tet_indiv_per_volume = single_video$Tet_indiv_per_volume)
}
# Describe code
ds_ecosystems_t0_elongated = ds_ecosystems_t0_elongated %>%
bind_rows()# --- CLEAN THE COLUMNS OF T0 --- #
ds_ecosystems_t0 = ds_ecosystems_t0_elongated %>%
select(file,
time_point,
day,
culture_ID,
video_replicate,
bioarea_per_volume,
indiv_per_volume,
all_of(protist_species_indiv_per_volume))
expect_equal(nrow(ds_ecosystems_t0),
sum(n_videos_taken_t0 * n_cultures))# --- IMPORT AND BIND ALL TIME POINTS BUT T0 --- #
ds_ecosystems_t1_to_t7 = NULL
for (time_point_i in time_points_without_t0) {
species_ID_13_threshold = read.csv(here("1_data",
"species_ID_13_threshold",
paste0("t", time_point_i, ".csv"))) %>%
rename(Ble_indiv_per_volume = Ble,
Cep_indiv_per_volume = Cep,
Col_indiv_per_volume = Col,
Eug_indiv_per_volume = Eug,
Eup_indiv_per_volume = Eup,
Lox_indiv_per_volume = Lox,
Pau_indiv_per_volume = Pau,
Pca_indiv_per_volume = Pca,
Spi_indiv_per_volume = Spi,
Spi_te_indiv_per_volume = Spi_te,
Tet_indiv_per_volume = Tet) %>%
select(file,
all_of(species_IDD_with_13_threshold_indiv_per_volume))
species_ID_40_threshold = read.csv(here("1_data",
"species_ID_40_threshold",
paste0("t", time_point_i, ".csv"))) %>%
rename(Ble_indiv_per_volume = Ble,
Cep_indiv_per_volume = Cep,
Col_indiv_per_volume = Col,
Eug_indiv_per_volume = Eug,
Eup_indiv_per_volume = Eup,
Lox_indiv_per_volume = Lox,
Pau_indiv_per_volume = Pau,
Pca_indiv_per_volume = Pca,
Spi_indiv_per_volume = Spi,
Spi_te_indiv_per_volume = Spi_te,
Tet_indiv_per_volume = Tet) %>%
select(file,
all_of(species_IDD_with_40_threshold_indiv_per_volume))
ds_ecosystems_t1_to_t7[[time_point_i]] = read.csv(here("1_data",
"ecosystems_13_threshold",
paste0("t", time_point_i, ".csv"))) %>%
arrange(file) %>%
mutate(video_replicate = rep(1 : time_point_day$video_replicates[time_point_i+1],
each = n_cultures),
day = time_point_day$day[time_point_day$time_point == time_point_i]) %>%
select(file,
time_point,
day,
video_replicate,
file,
culture_ID,
bioarea_per_volume,
indiv_per_volume)
ds_ecosystems_t1_to_t7[[time_point_i]] = ds_ecosystems_t1_to_t7[[time_point_i]] %>%
left_join(species_ID_13_threshold,
by = "file") %>%
left_join(species_ID_40_threshold,
by = "file")
}
ds_ecosystems_t1_to_t7 = ds_ecosystems_t1_to_t7 %>%
bind_rows()# --- BIND T0 WITH ALL OTHER TIME POINTS --- #
ds_ecosystems = rbind(ds_ecosystems_t0,
ds_ecosystems_t1_to_t7) %>%
left_join(ecosystems_info,
by = "culture_ID")
expect_equal(nrow(ds_ecosystems),
sum(sum(time_point_day$video_replicates) * n_cultures))# --- REODER AND RENAME COLUMNS --- #
ds_ecosystems = ds_ecosystems %>%
rename(ecosystem_size = patch_size,
ecosystem_size_ml = patch_size_volume) %>%
select(file,
time_point,
day,
disturbance,
culture_ID,
system_nr,
eco_metaeco_type,
ecosystem_size,
ecosystem_size_ml,
metaecosystem,
metaecosystem_type,
video_replicate,
bioarea_per_volume,
indiv_per_volume,
all_of(protist_species_indiv_per_volume)) %>%
rename(bioarea_µm2_per_μL = bioarea_per_volume) %>%
rename_all( ~ gsub("volume", "μL", .))# --- RENAME AND REODER LEVELS --- #
ds_ecosystems <- ds_ecosystems %>%
mutate(ecosystem_size = case_when(ecosystem_size == "S" ~ "Small",
ecosystem_size == "M" ~ "Medium",
ecosystem_size == "L" ~ "Large",
TRUE ~ ecosystem_size),
connection = case_when(eco_metaeco_type == "S" ~ "unconnected",
eco_metaeco_type == "M" ~ "unconnected",
eco_metaeco_type == "L" ~ "unconnected",
eco_metaeco_type == "S (S_S)" ~ "connected to small",
eco_metaeco_type == "S (S_L)" ~ "connected to large",
eco_metaeco_type == "M (M_M)" ~ "connected to medium",
eco_metaeco_type == "L (S_L)" ~ "connected to small",
eco_metaeco_type == "L (L_L)" ~ "connected to large"),
ecosystem_type = paste(ecosystem_size, connection),
metaecosystem_type = case_when(metaecosystem_type == "S_S" ~ "Small-Small",
metaecosystem_type == "M_M" ~ "Medium-Medium",
metaecosystem_type == "L_L" ~ "Large-Large",
metaecosystem_type == "S_L" ~ "Small-Large",
TRUE ~ metaecosystem_type),
time_point = as.numeric(str_extract(time_point, "\\d+")),
file = as.numeric(str_extract(file, "\\d+")))# --- CHANGE UNITS OF MEASUREMENTS TO ML --- #
ds_ecosystems = ds_ecosystems %>%
mutate(bioarea_µm2_per_ml = bioarea_µm2_per_μL * 10^3,
bioarea_mm2_per_ml = bioarea_µm2_per_ml * 10^(-6),
Ble_indiv_per_ml = Ble_indiv_per_μL * 10^3,
Cep_indiv_per_ml = Cep_indiv_per_μL * 10^3,
Col_indiv_per_ml = Col_indiv_per_μL * 10^3,
Eug_indiv_per_ml = Eug_indiv_per_μL * 10^3,
Eup_indiv_per_ml = Eup_indiv_per_μL * 10^3,
Lox_indiv_per_ml = Lox_indiv_per_μL * 10^3,
Pau_indiv_per_ml = Pau_indiv_per_μL * 10^3,
Pca_indiv_per_ml = Pca_indiv_per_μL * 10^3,
Spi_indiv_per_ml = Spi_indiv_per_μL * 10^3,
Spi_te_indiv_per_ml = Spi_te_indiv_per_μL * 10^3,
Tet_indiv_per_ml = Tet_indiv_per_μL * 10^3)# --- TAKE OFF PROBLEMATIC VIDEOS --- #
# Set up parameters
ds_ecosystems_before_taking_off_videos = ds_ecosystems
# Take off problematic videos
ds_ecosystems = ds_ecosystems %>%
filter(!(time_point %in% videos_to_take_off$time_point & file %in% videos_to_take_off$file))
# Test code
diff = setdiff(ds_ecosystems_before_taking_off_videos, ds_ecosystems)
expect_equal(nrow(videos_to_take_off),
nrow(expand.grid(diff$culture_ID, diff$time_point, diff$file) %>% unique()))# --- TAKE OFF PROBLEMATIC ECOSYSTEMS --- #
# Set up parameters
ds_ecosystems_before_taking_off_cultures = ds_ecosystems
# Take off problematic ecosystems
ds_ecosystems = ds_ecosystems %>%
filter(!culture_ID %in% ecosystems_to_take_off)
# Test code
expect_equal(setdiff(ds_ecosystems_before_taking_off_cultures,
ds_ecosystems) %>%
pull(culture_ID) %>%
unique(),
ecosystems_to_take_off)# --- AVERAGE VIDEOS --- #
# Average videos
ds_ecosystems = ds_ecosystems %>%
group_by(across(all_of(columns_ecosystems))) %>%
summarise(across(contains("_per_ml"), mean),
across(contains("_tot"), mean)) %>%
ungroup()
# Test code
expect_equal(nrow(ds_ecosystems),
(n_cultures - length(ecosystems_to_take_off)) * length(time_points))# --- ADD CONNECTION AND INDIVIDUALS --- #
ds_ecosystems = ds_ecosystems %>%
mutate(indiv_per_ml = !!rlang::parse_expr(paste(protist_species_indiv_per_ml,
collapse = " + ")))# --- CALCULATE TOTAL RESPONSE VARIABLE FOR THE WHOLE ECOSYSTEM --- #
ds_ecosystems = ds_ecosystems %>%
mutate(bioarea_tot_mm2 = bioarea_mm2_per_ml * ecosystem_size_ml,
indiv_tot = indiv_per_ml * ecosystem_size_ml,
Ble_tot_indiv = Ble_indiv_per_ml * ecosystem_size_ml,
Cep_tot_indiv = Cep_indiv_per_ml * ecosystem_size_ml,
Col_tot_indiv = Col_indiv_per_ml * ecosystem_size_ml,
Eug_tot_indiv = Eug_indiv_per_ml * ecosystem_size_ml,
Eup_tot_indiv = Eup_indiv_per_ml * ecosystem_size_ml,
Lox_tot_indiv = Lox_indiv_per_ml * ecosystem_size_ml,
Pau_tot_indiv = Pau_indiv_per_ml * ecosystem_size_ml,
Pca_tot_indiv = Pca_indiv_per_ml * ecosystem_size_ml,
Spi_tot_indiv = Spi_indiv_per_ml * ecosystem_size_ml,
Spi_te_tot_indiv = Spi_te_indiv_per_ml * ecosystem_size_ml,
Tet_tot_indiv = Tet_indiv_per_ml * ecosystem_size_ml)# --- CALCULATE SPECIES DOMINANCE --- #
# Calculate species dominance
ds_ecosystems = ds_ecosystems %>%
mutate(across(.cols = all_of(protist_species_indiv_per_ml),
.fns = list(dominance = ~ (. / indiv_per_ml) * 100),
.names = "{col}_dominance"))
# Test code
expect_equal(unique(ds_ecosystems$Ble_indiv_per_ml_dominance[ds_ecosystems$indiv_per_ml == 0]), NaN)
if (FALSE %in% unique((ds_ecosystems$Ble_indiv_per_ml/ds_ecosystems$indiv_per_ml) *100 == ds_ecosystems$Ble_indiv_per_ml_dominance)) stop()# --- CALCULATE ALPHA DIVERSITY (SHANNON, SIMPSON, INVERSE SIMPSON, EVENNESS) --- #
# Set up parameters for testing
n_rows_ds_ecosystems_before_calculating_alpha = nrow(ds_ecosystems)
# Calculate alpha diversity
ds_ecosystems = calculate.alpha.diversity()
# Test code
expect_equal(max(ds_ecosystems$species_richness),
length(protist_species))
expect_equal(nrow(ds_ecosystems),
n_rows_ds_ecosystems_before_calculating_alpha)# --- CALCULATE MEDIAN BODY SIZE --- #
# Set up parameters for testing
n_rows_ds_ecosystems_before_median_size = nrow(ds_ecosystems)
# Calculate median body size
ds_median_body_size = ds_individuals %>%
group_by(time_point,
culture_ID,
file) %>%
summarise(median_body_area_µm2 = median(body_area_µm2)) %>%
group_by(time_point,
culture_ID) %>%
summarise(median_body_area_µm2 = mean(median_body_area_µm2))
# Test code
expect_true(nrow(ds_median_body_size) <= nrow(ds_ecosystems)) #Ds median body size could be less because some cultures might be crashed and not have any individual.
# Set up parameters for testing
ds_ecosystems_before_full_join = ds_ecosystems
# Join the median body size with ds_ecosystems
ds_ecosystems = full_join(ds_ecosystems, ds_median_body_size)
# Test code
expect_equal(nrow(ds_ecosystems),
n_rows_ds_ecosystems_before_median_size)# --- CALCULATE AUTO/HETEROTROPHIC RATIO --- #
ds_ecosystems = ds_ecosystems %>%
mutate(auto_hetero_ratio = (Eug_indiv_per_ml + Eup_indiv_per_ml) /
(Ble_indiv_per_ml +
Cep_indiv_per_ml +
Col_indiv_per_ml +
Lox_indiv_per_ml +
Pau_indiv_per_ml +
Pca_indiv_per_ml +
Spi_indiv_per_ml +
Spi_te_indiv_per_ml +
Tet_indiv_per_ml))# --- ADD EVAPORATION RATES --- #
# Manipulate evaporation rates data
ds_for_evaporation = read.csv(here("1_data",
"evaporation",
"water_addition.csv")) %>%
pivot_longer(cols = starts_with("water_add_after_t"),
names_to = "time_point",
values_to = "water_addition_ml") %>%
mutate(time_point = as.double(str_extract(time_point, "\\d+")) + 1)
# Add evaporation rates to ds_ecosystems
ds_ecosystems = ds_ecosystems %>%
left_join(ds_for_evaporation)# --- ADD TRANSFORMED VARIABLES --- #
ds_ecosystems = ds_ecosystems %>%
mutate(sqrt_bioarea_mm2_per_ml = sqrt(bioarea_mm2_per_ml),
sqrt_auto_hetero_ratio = sqrt(auto_hetero_ratio))Patch effect sizes (ds_ecosystems_effect_size)
In this dataset (ds_ecosystems_effect_size) each row
represents a treatment at a time point. It contains the effect size of
the connection of a ecosystem (connected vs unconnected).
# --- CALCULATE THE MEAN & SD OF RESPONSE VARIABLES FOR EACH TREATMENT/CONTROL AT EACH TIME POINT --- #
ds_ecosystems_effect_size = NULL
variable_nr = 0
for (variable_i in variables_ecosystems) {
variable_nr = variable_nr + 1
ds_ecosystems_effect_size[[variable_nr]] = ds_ecosystems %>%
filter(time_point >= 1,
!is.na(!!sym(variable_i))) %>%
group_by(across(all_of(columns_ecosystems[columns_ecosystems != "culture_ID" &
columns_ecosystems != "system_nr"]))) %>%
summarise(across(all_of(variable_i),
list(mean = mean,
sd = sd)),
sample_size = n()) %>%
rename_with( ~ paste0(variable_i, "_sample_size"),
matches("sample_size"))
}
ds_ecosystems_effect_size <- reduce(ds_ecosystems_effect_size,
full_join,
by = columns_ecosystems[columns_ecosystems != "culture_ID" & columns_ecosystems != "system_nr"])
expect_equal(nrow(ds_ecosystems_effect_size),
n_ecosystem_types * (n_time_points-1) * n_disturbance_levels)# --- CALCULATE THE EFFECT SIZE (HEDGE'S D) FOR EACH TREATMENT AT EACH TIME POINT --- #
for (variable_i in variables_ecosystems) {
ds_ecosystems_effect_size <- ds_ecosystems_effect_size %>%
mutate(!!paste0(variable_i, "_d") := NA,
!!paste0(variable_i, "_d_upper") := NA,
!!paste0(variable_i, "_d_lower") := NA)
}
row_i = 0
for (treatment_selected in treatments_and_controls$treatment) {
for (time_point_selected in time_points) {
row_i = row_i + 1
control_input = treatments_and_controls$control[treatments_and_controls$treatment == treatment_selected]
treatment_row = ds_ecosystems_effect_size %>%
filter(ecosystem_type == treatment_selected,
time_point == time_point_selected)
control_row = ds_ecosystems_effect_size %>%
filter(ecosystem_type == control_input,
time_point == time_point_selected)
for (response_variable in variables_ecosystems) {
hedges_d = calculate.hedges_d(treatment_row[[paste0(response_variable, "_mean")]],
treatment_row[[paste0(response_variable, "_sd")]],
treatment_row[[paste0(response_variable, "_sample_size")]],
control_row[[paste0(response_variable, "_mean")]],
control_row[[paste0(response_variable, "_sd")]],
control_row[[paste0(response_variable, "_sample_size")]])
ds_ecosystems_effect_size[[paste0(response_variable, "_d")]][
ds_ecosystems_effect_size$ecosystem_type == treatment_selected &
ds_ecosystems_effect_size$time_point == time_point_selected] =
hedges_d$d
ds_ecosystems_effect_size[[paste0(response_variable, "_d_upper")]][
ds_ecosystems_effect_size$ecosystem_type == treatment_selected &
ds_ecosystems_effect_size$time_point == time_point_selected] =
hedges_d$upper_CI
ds_ecosystems_effect_size[[paste0(response_variable, "_d_lower")]][
ds_ecosystems_effect_size$ecosystem_type == treatment_selected &
ds_ecosystems_effect_size$time_point == time_point_selected] =
hedges_d$lower_CI
}
}
}
expect_equal(nrow(ds_ecosystems_effect_size),
n_ecosystem_types * (n_time_points-1) * n_disturbance_levels)Meta-ecosystems (ds_metaecosystems)
In this dataset (ds_metaecosystems) each row represents
a meta-ecosystem or a two-ecosystem unconnected system at a time
point.
# --- FIND IDS OF UNCONNECTED ECOSYSTEMS --- #
ID_unconnected_S_low = ds_ecosystems %>%
filter(ecosystem_type == "Small unconnected",
disturbance == "low") %>%
pull(culture_ID) %>%
unique()
ID_unconnected_M_low = ds_ecosystems %>%
filter(ecosystem_type == "Medium unconnected",
disturbance == "low") %>%
pull(culture_ID) %>%
unique()
ID_unconnected_L_low = ds_ecosystems %>%
filter(ecosystem_type == "Large unconnected",
disturbance == "low") %>%
pull(culture_ID) %>%
unique()
ID_unconnected_S_high = ds_ecosystems %>%
filter(ecosystem_type == "Small unconnected",
disturbance == "high") %>%
pull(culture_ID) %>%
unique()
ID_unconnected_M_high = ds_ecosystems %>%
filter(ecosystem_type == "Medium unconnected",
disturbance == "high") %>%
pull(culture_ID) %>%
unique()
ID_unconnected_L_high = ds_ecosystems %>%
filter(ecosystem_type == "Large unconnected",
disturbance == "high") %>%
pull(culture_ID) %>%
unique()# --- FIND COMBINATIONS OF ECOSYSTEMS TO CREATE UNCONNECTED META-ECOSYSTEMS --- #
combinations_S_and_L_low = crossing(ID_unconnected_S_low,
ID_unconnected_L_low) %>%
mutate(disturbance = "low",
metaecosystem_type = "Small-Large",
connection = "unconnected") %>%
rename(ID_first_ecosystem = ID_unconnected_S_low,
ID_second_ecosystem = ID_unconnected_L_low) %>%
select(disturbance,
metaecosystem_type,
connection,
ID_first_ecosystem,
ID_second_ecosystem)
combinations_S_and_L_high = crossing(ID_unconnected_S_high,
ID_unconnected_L_high) %>%
mutate(disturbance = "high",
metaecosystem_type = "Small-Large",
connection = "unconnected") %>%
rename(ID_first_ecosystem = ID_unconnected_S_high,
ID_second_ecosystem = ID_unconnected_L_high) %>%
select(disturbance,
metaecosystem_type,
connection,
ID_first_ecosystem,
ID_second_ecosystem)
combinations_M_and_M_low = combinat::combn(ID_unconnected_M_low,
m = 2) %>%
t() %>%
as.data.frame() %>%
rename(ID_first_ecosystem = V1,
ID_second_ecosystem = V2) %>%
mutate(disturbance = "low",
metaecosystem_type = "Medium-Medium",
connection = "unconnected") %>%
select(disturbance,
metaecosystem_type,
connection,
ID_first_ecosystem,
ID_second_ecosystem)
combinations_M_and_M_high = combinat::combn(ID_unconnected_M_high,
m = 2) %>%
t() %>%
as.data.frame() %>%
rename(ID_first_ecosystem = V1,
ID_second_ecosystem = V2) %>%
mutate(disturbance = "high",
metaecosystem_type = "Medium-Medium",
connection = "unconnected") %>%
select(disturbance,
metaecosystem_type,
connection,
ID_first_ecosystem,
ID_second_ecosystem)# --- BIND ECOSYSTEM COMBINATIONS --- #
combinations_unconnected_metaeco = rbind(combinations_S_and_L_low,
combinations_S_and_L_high,
combinations_M_and_M_low,
combinations_M_and_M_high) %>%
mutate(system_nr = 1001:(1000 + nrow(.))) %>%
select(system_nr,
disturbance,
metaecosystem_type,
connection,
ID_first_ecosystem,
ID_second_ecosystem)# --- FIND COMBINATIONS OF ECOSYSTEMS TO CREATE CONNECTED META-ECOSYSTEMS --- #
combinations_connected_metaeco = ds_ecosystems %>%
filter(time_point == 0,
metaecosystem == "yes") %>%
select(system_nr,
disturbance,
metaecosystem_type,
culture_ID) %>%
group_by(system_nr,
disturbance,
metaecosystem_type) %>%
summarise(ID_first_ecosystem = (mean(culture_ID) - 0.5),
ID_second_ecosystem = (mean(culture_ID) + 0.5)) %>%
mutate(connection = "connected") %>%
as.data.frame()# --- BIND COMBINATIONS OF ECOSYSTEMS TO CREATE UNCONNECTED & CONNECTED META-ECOSYSTEMS --- #
ecos_combin = rbind(combinations_unconnected_metaeco,
combinations_connected_metaeco) %>%
mutate(ecosystems_combined = paste0(ID_first_ecosystem, "|", ID_second_ecosystem))
n_ecosystems_combinations = nrow(ecos_combin)# --- CREATE SETS FOR SMALL-LARGE UNCONNECTED META-ECOSYSTEMS --- #
# Create sets for Small-Large unconnected meta-ecosystems where in each set a small and a large ecosystems are paired differently (keep the small ecosystems on the same order and perform permutations on the large ecosystems)
SL_unconn_ecos_comb_sets <- vector("list",
length(disturbance_levels))
for (disturbance_i in 1:length(disturbance_levels)) {
ID_small_ecosystems = ds_ecosystems %>%
filter(disturbance == disturbance_levels[disturbance_i],
ecosystem_type == "Small unconnected") %>%
pull(culture_ID) %>%
unique()
ID_large_ecosystems = ds_ecosystems %>%
filter(disturbance == disturbance_levels[disturbance_i],
ecosystem_type == "Large unconnected") %>%
pull(culture_ID) %>%
unique()
#Force small and large ecosystems vectors to have the same length
length_difference <- length(ID_small_ecosystems) - length(ID_large_ecosystems)
if (length_difference > 0) {
ID_large_ecosystems = c(ID_large_ecosystems,
rep("Patch taken off",
times = abs(length(ID_small_ecosystems) -
length(ID_large_ecosystems))))
} else if (length_difference < 0) {
ID_small_ecosystems = c(ID_small_ecosystems,
rep("Patch taken off",
times = abs(length(ID_large_ecosystems) -
length(ID_small_ecosystems))))
}
# Create dataframe
permutations_large = permn(ID_large_ecosystems)
SL_unconn_ecos_comb_sets[[disturbance_i]] = data.frame(
disturbance = disturbance_levels[disturbance_i],
metaecosystem_type = "Small-Large",
connection = "unconnected",
ID_first_ecosystem = rep(ID_small_ecosystems,
times = length(permutations_large)),
ID_second_ecosystem = unlist(permutations_large),
set = rep(1 : length(permutations_large),
each = length(ID_small_ecosystems)))
expect_equal(nrow(SL_unconn_ecos_comb_sets[[disturbance_i]]),
length(ID_small_ecosystems) * length(permutations_large))
SL_unconn_ecos_comb_sets[[disturbance_i]] = SL_unconn_ecos_comb_sets[[disturbance_i]] %>%
filter(!ID_first_ecosystem == "Patch taken off",
!ID_second_ecosystem == "Patch taken off") %>%
mutate(ID_first_ecosystem = as.double(ID_first_ecosystem),
ID_second_ecosystem = as.double(ID_second_ecosystem)) %>%
full_join(ecos_combin %>%
filter(disturbance == disturbance_levels[disturbance_i],
metaecosystem_type == "Small-Large",
connection == "unconnected")) #Add system_nr & ecosystems_combined
}
SL_unconn_ecos_comb_sets_before_binding = SL_unconn_ecos_comb_sets
SL_unconn_ecos_comb_sets = SL_unconn_ecos_comb_sets %>%
bind_rows()# --- TEST CODE --- #
expect_equal(nrow(SL_unconn_ecos_comb_sets),
nrow(SL_unconn_ecos_comb_sets_before_binding[[1]]) + nrow(SL_unconn_ecos_comb_sets_before_binding[[2]]))
expect_equal(length(SL_unconn_ecos_comb_sets %>%
pull(system_nr) %>%
unique()),
length(ecos_combin %>%
filter(metaecosystem_type == "Small-Large",
connection == "unconnected") %>%
pull(system_nr) %>%
unique()))# --- CREATE SETS FOR MEDIUM-MEDIUM UNCONNECTED META-ECOSYSTEMS --- #
# Create sets for Medium-Medium unconnected meta-ecosystems where in each set two different medium ecosystems are paired. To do so, initialise MM_unconn_ecos_comb_sets. Assign 10^4 rows to each matrix so that we have enough rows not to run out of them when we try to assign values to them. Assign 4 columns which will include culture_ID of the first system, second culture_ID of the fist system, culture_ID of the second system, and second culture_ID of the second system.
MM_unconn_ecos_comb_sets = NULL
for(disturbance_i in 1:length(disturbance_levels)){
MM_unconn_ecos_comb_sets[[disturbance_i]] <- matrix(nrow = 10 ^ 4,
ncol = 4)
}
for (disturbance_i in 1:length(disturbance_levels)) {
ID_medium_ecosystems = ds_ecosystems %>%
filter(disturbance == disturbance_levels[disturbance_i],
ecosystem_type == "Medium unconnected") %>%
pull(culture_ID) %>%
unique()
MM_unconnected_systems = combn(ID_medium_ecosystems,
2) %>%
t()
matrix_row = 0
for (first_system_i in 1:nrow(MM_unconnected_systems)) {
#Find culture IDs of the first system (what's the first system?)
first_system = MM_unconnected_systems[first_system_i, ]
for (second_system_i in 1:nrow(MM_unconnected_systems)) {
#Find culture IDs of the second system (what's the second system?)
second_system = MM_unconnected_systems[second_system_i, ]
shared_elements_among_systems = intersect(first_system,
second_system)
if (length(shared_elements_among_systems) == 0) {
matrix_row = matrix_row + 1
#Make first and second system into a set
MM_unconn_ecos_comb_sets[[disturbance_i]][matrix_row,] = c(first_system,
second_system)
print(MM_unconn_ecos_comb_sets[[disturbance_i]][matrix_row,])
}
}
}
#Tidy the dataset with all the ecosystem combinations
MM_unconn_ecos_comb_sets[[disturbance_i]] = MM_unconn_ecos_comb_sets[[disturbance_i]] %>%
as.data.frame() %>%
drop_na()
# Test code
expect_equal(MM_unconn_ecos_comb_sets[[disturbance_i]] %>%
filter(V1 == V2 |
V1 == V3 |
V1 == V4 |
V2 == V3 |
V2 == V4 |
V3 == V4) %>%
nrow(),
0)
#Reorder the dataset with all the ecosystem combinations
MM_unconn_ecos_comb_sets_reordered = data.frame(ID_first_ecosystem = NA,
ID_second_ecosystem = NA,
set = NA)
for (set_input in 1:nrow(MM_unconn_ecos_comb_sets[[disturbance_i]])) {
MM_unconn_ecos_comb_sets_reordered = MM_unconn_ecos_comb_sets_reordered %>%
add_row(ID_first_ecosystem = MM_unconn_ecos_comb_sets[[disturbance_i]][set_input, 1],
ID_second_ecosystem = MM_unconn_ecos_comb_sets[[disturbance_i]][set_input, 2],
set = set_input) %>%
add_row(ID_first_ecosystem = MM_unconn_ecos_comb_sets[[disturbance_i]][set_input, 3],
ID_second_ecosystem = MM_unconn_ecos_comb_sets[[disturbance_i]][set_input, 4],
set = set_input)
}
#Add to a list
MM_unconn_ecos_comb_sets[[disturbance_i]] = MM_unconn_ecos_comb_sets_reordered %>%
drop_na() %>%
mutate(disturbance = disturbance_levels[disturbance_i],
metaecosystem_type = "Medium-Medium",
connection = "unconnected")
#Add system nr
ID_combinations_MM_unconnected = ecos_combin %>%
filter(disturbance == disturbance_levels[disturbance_i],
metaecosystem_type == "Medium-Medium",
connection == "unconnected")
MM_unconn_ecos_comb_sets[[disturbance_i]] = full_join(MM_unconn_ecos_comb_sets[[disturbance_i]],
ID_combinations_MM_unconnected)
}## [1] 6 7 8 9
## [1] 6 7 8 10
## [1] 6 7 9 10
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## [1] 61 62 63 64
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## [1] 61 65 63 64
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## [1] 62 64 61 65
## [1] 62 64 63 65
## [1] 62 65 61 63
## [1] 62 65 61 64
## [1] 62 65 63 64
## [1] 63 64 61 62
## [1] 63 64 61 65
## [1] 63 64 62 65
## [1] 63 65 61 62
## [1] 63 65 61 64
## [1] 63 65 62 64
## [1] 64 65 61 62
## [1] 64 65 61 63
## [1] 64 65 62 63#Bind all sets of MM unconnected
MM_unconn_ecos_comb_sets = MM_unconn_ecos_comb_sets %>%
bind_rows()# --- TEST CODE --- #
expect_equal(length(MM_unconn_ecos_comb_sets %>%
pull(system_nr) %>%
unique()),
length(ecos_combin %>%
filter(metaecosystem_type == "Medium-Medium",
connection == "unconnected") %>%
pull(system_nr) %>%
unique()))# --- BIND SL AND MM UNCONNECTED META-ECOSYSTEMS SETS --- #
ecos_combin_unconn_sets = rbind(SL_unconn_ecos_comb_sets,
MM_unconn_ecos_comb_sets) %>%
select(disturbance,
metaecosystem_type,
connection,
set,
system_nr,
ID_first_ecosystem,
ID_second_ecosystem)# --- FIND SETS OF SETS --- #
# Find the number of combinations of SL & MM low or high disturbance
n_SL_low_sets = ecos_combin_unconn_sets %>%
filter(metaecosystem_type == "Small-Large",
disturbance == "low") %>%
pull(set) %>%
max()
n_SL_high_sets = ecos_combin_unconn_sets %>%
filter(metaecosystem_type == "Small-Large",
disturbance == "high") %>%
pull(set) %>%
max()
n_MM_low_sets = ecos_combin_unconn_sets %>%
filter(metaecosystem_type == "Medium-Medium",
disturbance == "low") %>%
pull(set) %>%
max()
n_MM_high_sets = ecos_combin_unconn_sets %>%
filter(metaecosystem_type == "Medium-Medium",
disturbance == "high") %>%
pull(set) %>%
max()
# Find combinations of sets
ecos_combin_unconn_sets_of_sets <- expand.grid(set_SL_low = 1:n_SL_low_sets,
set_SL_high = 1:n_SL_high_sets,
set_MM_low = 1:n_MM_low_sets,
set_MM_high = 1:n_MM_high_sets) %>%
print()## set_SL_low set_SL_high set_MM_low set_MM_high
## 1 1 1 1 1
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## [ reached 'max' / getOption("max.print") -- omitted 12935001 rows ]Each row is a meta-ecosystem.
It contains also “fake” meta-ecosystems which I created from unconnected ecosystems (
metaecosystem type = Small-Large unconnected&metaecosystem type = Medium-Medium unconnected).Warning appear after the following code, as:
- alpha diversity is computed for a culture that is totally crashed, so it has not meaning.
- beta diversity is computed between a culture that is still alive and one that is totally crashed. Because one of the cultures is totally crashed, the Bray-Curtis index is 1.
# --- COMPUTE META-ECOSYSTEMS FOR EACH TIME POINT --- #
ds_metaecosystems = NULL
row_i = 0
for (combination_i in 1:n_ecosystems_combinations) {
for (time_point_selected in time_points) {
row_i = row_i + 1
current_day = sampling_days[time_point_selected + 1]
current_system_nr = ecos_combin[combination_i, ]$system_nr
current_combination = ecos_combin[combination_i, ]$ecosystems_combined
current_disturbance = ecos_combin[combination_i, ]$disturbance
current_metaeco_type = ecos_combin[combination_i, ]$metaecosystem_type
current_connection = ecos_combin[combination_i, ]$connection
current_IDs = c(ecos_combin[combination_i, ]$ID_first_ecosystem,
ecos_combin[combination_i, ]$ID_second_ecosystem)
if (current_system_nr %in% metaecosystems_to_take_off)
next
if (current_IDs[1] == current_IDs[2])
next
species_vector_two_ecosystems = ds_ecosystems %>%
filter(time_point == time_point_selected,
culture_ID %in% current_IDs) %>%
ungroup() %>%
select(all_of(protist_species_indiv_per_ml))
absence_presence_two_ecosystems <-
ifelse(species_vector_two_ecosystems > 0, 1, 0)
#Alpha diversity: Shannon (mean between the two ecosystems)
shannon_ecosystem_1 = diversity(species_vector_two_ecosystems[1, ], index = "shannon")
shannon_ecosystem_2 = diversity(species_vector_two_ecosystems[2, ], index = "shannon")
shannon_value = (shannon_ecosystem_1 + shannon_ecosystem_2) / 2
#Alpha diversity: Species richness (mean between the two ecosystems)
richness_ecosystem_1 = specnumber(species_vector_two_ecosystems[1, ])
richness_ecosystem_2 = specnumber(species_vector_two_ecosystems[2, ])
mean_richness_value = (richness_ecosystem_1 + richness_ecosystem_2) / 2
#Beta diversity: Jaccard
jaccard_index_value = vegdist(species_vector_two_ecosystems,
method = "jaccard") %>%
as.numeric()
#Beta diversity: Bray Curtis
bray_curtis_value = vegdist(species_vector_two_ecosystems,
method = "bray") %>%
as.numeric()
#Beta diversity: partitioning of beta diversity from Sorensen index into turnover (Simpson pair-wise dissimilarity) and nestedness (nestedness-fraction of Sorensen)
betapart_core_object = betapart.core(absence_presence_two_ecosystems)
beta_spatial_turnover_value = beta.pair(betapart_core_object)$beta.sim %>% as.double()
beta_nestedness_value = beta.pair(betapart_core_object)$beta.sne %>% as.double()
beta_total_value = beta.pair(betapart_core_object)$beta.sor %>% as.double()
#Gamma diversity: Meta-ecosystem richness
metaecosystem_richness_value = colSums(species_vector_two_ecosystems) %>%
specnumber()
#Put everything together
ds_metaecosystems[[row_i]] = ds_ecosystems %>%
filter(culture_ID %in% current_IDs,
time_point == time_point_selected) %>%
summarise(total_metaecosystem_bioarea_mm2 = sum(bioarea_tot_mm2),
total_metaecosystem_Ble_indiv = sum(Ble_tot_indiv),
total_metaecosystem_Cep_indiv = sum(Cep_tot_indiv),
total_metaecosystem_Col_indiv = sum(Col_tot_indiv),
total_metaecosystem_Eug_indiv = sum(Eug_tot_indiv),
total_metaecosystem_Eup_indiv = sum(Eup_tot_indiv),
total_metaecosystem_Lox_indiv = sum(Lox_tot_indiv),
total_metaecosystem_Pau_indiv = sum(Pau_tot_indiv),
total_metaecosystem_Pca_indiv = sum(Pca_tot_indiv),
total_metaecosystem_Spi_indiv = sum(Spi_tot_indiv),
total_metaecosystem_Spi_te_indiv = sum(Spi_te_tot_indiv),
total_metaecosystem_Tet_indiv = sum(Tet_tot_indiv),
total_water_addition_ml = sum(water_addition_ml)) %>%
mutate(system_nr = current_system_nr,
ecosystems_combined = current_combination,
metaecosystem_type = current_metaeco_type,
ecosystem_size_symmetry = case_when(metaecosystem_type == "Small-Large" ~ "asymmetric",
metaecosystem_type == "Medium-Medium" ~ "symmetric",
metaecosystem_type == "Small-Small" ~ "symmetric",
metaecosystem_type == "Large-Large" ~ "symmetric"),
connection = current_connection,
disturbance = current_disturbance,
time_point = time_point_selected,
day = current_day,
jaccard_index = jaccard_index_value,
bray_curtis = bray_curtis_value,
beta_spatial_turnover = beta_spatial_turnover_value,
beta_nestedness = beta_nestedness_value,
beta_total = beta_total_value,
metaecosystem_richness = metaecosystem_richness_value,
mean_shannon = shannon_value,
mean_richness = mean_richness_value) %>%
ungroup()
}
}## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): missing
## values in results## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): missing
## values in results## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): missing
## values in results## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): missing
## values in results## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_ecosystems, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_ecosystems, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"# Tidy up
ds_metaecosystems = ds_metaecosystems %>%
bind_rows() %>%
as.data.frame() %>%
select(time_point,
day,
system_nr,
ecosystems_combined,
disturbance,
metaecosystem_type,
ecosystem_size_symmetry,
connection,
mean_shannon,
mean_richness,
jaccard_index,
bray_curtis,
beta_spatial_turnover,
beta_nestedness,
beta_total,
metaecosystem_richness,
total_metaecosystem_bioarea_mm2,
paste0("total_metaecosystem_", protist_species, "_indiv"),
total_water_addition_ml)# --- TEST CODE --- #
expect_equal(nrow(ds_metaecosystems),
n_time_points * n_ecosystems_combinations)Meta-ecosystems
metaecosystem_type_selected = c("Medium-Medium", "Small-Large")Alpha
response_variable_selected = "mean_shannon"Plot original data - means
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
# Create plots
p1=plot.metaecos.points(ds_metaecosystems %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.points(ds_metaecosystems %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(0.9, 1),
common.legend = TRUE,
align = "v")
Plot original data - single replicates
# --- ORIGINAL DATA - PLOT SINGLE REPLICATES --- #
p1=plot.metaecos.replicates(ds_metaecosystems %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.replicates(ds_metaecosystems %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(0.9, 1),
common.legend = TRUE,
align = "v")
Prepare data for analysis
# --- PREPARE DATA --- #
# Add baselines
baselines = ds_metaecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(system_nr,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_metaecosystems %>%
left_join(baselines)
# Filter and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
metaecosystem_type %in% metaecosystem_type_selected,
#!is.na(total_water_addition_ml),
!is.na(!!sym(response_variable_selected))) %>%
mutate(metaecosystem_type = case_when(metaecosystem_type == "Small-Large unconnected" ~ "SL unc",
metaecosystem_type == "Medium-Medium unconnected" ~ "MM unc",
metaecosystem_type == "Small-Small meta-ecosystem" ~ "SS con",
metaecosystem_type == "Medium-Medium meta-ecosystem" ~ "MM con",
metaecosystem_type == "Large-Large meta-ecosystem" ~ "LL con",
metaecosystem_type == "Small-Large meta-ecosystem" ~ "SL con",
TRUE ~ metaecosystem_type),
type_conn = paste(metaecosystem_type, connection))Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI OF FILTERED DATA --- #
p1=plot.metaecos.points(data_for_analysis %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.points(data_for_analysis %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- DEFINE MODEL FORMULA --- #
formula = paste("get(response_variable_selected) ~",
"type_conn * disturbance * day +",
"(day | baseline) +",
"(day | system_nr)") %>%
print()## [1] "get(response_variable_selected) ~ type_conn * disturbance * day + (day | baseline) + (day | system_nr)"# --- FIND ECOSYSTEM COMBINATIONS FOR ANALYSIS --- #
# Set up parameters
bootstrap_iterations = 1000
# Produce random (bootstrapped) rows
random_sets_of_sets <- runif(bootstrap_iterations,
min = 1,
max = nrow(ecos_combin_unconn_sets_of_sets)) %>%
round()
# Filter combinations according to the random rows
sets_of_sets_filtered = ecos_combin_unconn_sets_of_sets %>%
filter(row_number() %in% random_sets_of_sets)# --- DEFINE WHERE TO SAVE (AND FROM WHERE TO LATER READ) THE RESULTS OF THE ANALYSIS --- #
file_path = here("3_results", "tables",
paste0("results_metaeco_",
response_variable_selected,
"_",
bootstrap_iterations,
"_iterations",
".csv"))# --- FIND MODEL STATISTICS THROUGH MULTIPLE ITERATIONS RESHUFFLING ECOSYSTEM COMBINATIONS --- #
# Set parameters and initialise
results_table = list()
failed_optimizers = 0
# Fit models
for (comb_i in 1:bootstrap_iterations) {
# Find system numbers
system_nr_unconn_selected = ecos_combin_unconn_sets %>%
filter(metaecosystem_type == "Small-Large" &
disturbance == "low" &
set == sets_of_sets_filtered[comb_i, ]$set_SL_low |
metaecosystem_type == "Small-Large" &
disturbance == "high" &
set == sets_of_sets_filtered[comb_i, ]$set_SL_high |
metaecosystem_type == "Medium-Medium" &
disturbance == "low" &
set == sets_of_sets_filtered[comb_i, ]$set_MM_low |
metaecosystem_type == "Medium-Medium" &
disturbance == "high" &
set == sets_of_sets_filtered[comb_i, ]$set_MM_high) %>%
pull(system_nr)
# Prepare data for analysis
data_for_analysis_comb_i = data_for_analysis %>%
filter(system_nr %in% system_nr_unconn_selected | connection == "connected")
# Run model
model = try.different.optimizers.metaecos(data_for_analysis_comb_i,
formula)
# If all the optimisers fail, move on to the next iteration
if (is.null(model)) {
cat("This model could not be fitted with any optimiser (",
system_nr_unconn_selected,
") \n")
failed_optimizers = failed_optimizers + 1
next
}
# Residuals - show Q-Q plot
# print(qqnorm(resid(model))); print(qqline(resid(model)))
# Residuals - save Residuals vs Fitted plot
# plot = data_for_analysis_comb_i %>%
# mutate(predicted = fitted(model),
# residuals = resid(model)) %>%
# ggplot(aes(x = predicted,
# y = residuals)) +
# geom_point()
#
# ggsave(here("3_results",
# "residual_plots",
# paste0("res_vs_fit_",
# response_variable_selected,
# "_combination_",
# comb_i,
# ".png")),
# plot = plot,
# width = 8,
# height = 6)
# Set up contrasts
emmeans_output = emmeans(model,
specs = ~ type_conn * day * disturbance ,
method = "pairwise",
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
summary(emmeans_output)
zeros = rep(0, 8)
high_MM_conn = zeros; high_MM_conn[1] = 1
high_MM_unc = zeros; high_MM_unc[2] = 1
high_SL_conn = zeros; high_SL_conn[3] = 1
high_SL_unc = zeros; high_SL_unc[4] = 1
low_MM_conn = zeros; low_MM_conn[5] = 1
low_MM_unc = zeros; low_MM_unc[6] = 1
low_SL_conn = zeros; low_SL_conn[7] = 1
low_SL_unc = zeros; low_SL_unc[8] = 1
# Save contrasts in a table
results_table[[comb_i]] = contrast(
emmeans_output,
method = list("high SL_conn - SL_unc" = high_SL_conn - high_SL_unc,
"high MM_conn - MM_unc" = high_MM_conn - high_MM_unc,
"low SL_conn - SL_unc" = low_SL_conn - low_SL_unc,
"low MM_conn - MM_unc" = low_MM_conn - low_MM_unc)) %>%
as.data.frame() %>%
mutate(combination = comb_i,
system_nr_unconnected_systems = paste(system_nr_unconn_selected, collapse = ", "))
}
# Tell how many optimizers failed
print(paste((failed_optimizers/bootstrap_iterations * 100),
"% of combinations created models that could not be fit"))
# Bind rows within the list results_table
results_table = results_table %>%
bind_rows()
# Save the results
write.csv(results_table,
file = file_path,
row.names = F)# --- READ THE RESULTS AFTER LENGTHY COMPUTATION --- #
results_table = read.csv(file = file_path)Show iterated results - p value distributions
# --- SHOW ITERATED RESULTS - P VALUE DISTRIBUTIONS --- #
results_table %>%
filter(contrast == "high MM_conn - MM_unc") %>%
pull(p.value) %>%
hist(main = "p values high MM_conn - MM_unc")
results_table %>%
filter(contrast == "high SL_conn - SL_unc") %>%
pull(p.value) %>%
hist(main = "p values high SL_conn - SL_unc")
results_table %>%
filter(contrast == "low MM_conn - MM_unc") %>%
pull(p.value) %>%
hist(main = "p values low MM_conn - MM_unc")
results_table %>%
filter(contrast == "low SL_conn - SL_unc") %>%
pull(p.value) %>%
hist(main = "p values low SL_conn vs SL_unc")
Show iterated results - table with averaged values
# --- SHOW ITERATED RESULTS - TABLE WITH AVERAGED VALUES --- #
results_table %>%
group_by(contrast) %>%
summarise(estimate = median(estimate),
SE = median(SE),
df = median(df),
t.ratio = median(t.ratio),
p.value = median(p.value)) %>%
as.data.frame() %>%
mutate(estimate = round(estimate, digits = 3),
SE = round(SE, digits = 3),
df = round(df, digits = 3),
t.ratio = round(t.ratio, digits = 3),
p.value = round(p.value, digits = 3),
evidence = "",
evidence = ifelse(p.value > 0.1,
"none",
evidence),
evidence = ifelse(p.value < 0.1,
"* weak",
evidence),
evidence = ifelse(p.value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p.value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p.value < 0.001,
"**** very strong",
evidence),
p.value = ifelse(p.value < 0.001,
"< 0.001",
p.value))## contrast estimate SE df t.ratio p.value evidence
## 1 high MM_conn - MM_unc 0.115 0.078 37.56 1.497 0.142 none
## 2 high SL_conn - SL_unc 0.190 0.063 37.56 3.034 0.004 *** strong
## 3 low MM_conn - MM_unc -0.003 0.078 37.56 -0.034 0.933 none
## 4 low SL_conn - SL_unc 0.182 0.059 37.56 3.075 0.004 *** strongBeta
response_variable_selected = "bray_curtis"Plot original data - means
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
# Create plots
p1=plot.metaecos.points(ds_metaecosystems %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.points(ds_metaecosystems %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(0.9, 1),
common.legend = TRUE,
align = "v")
Plot original data - single replicates
# --- ORIGINAL DATA - PLOT SINGLE REPLICATES --- #
p1=plot.metaecos.replicates(ds_metaecosystems %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.replicates(ds_metaecosystems %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(0.9, 1),
common.legend = TRUE,
align = "v")
Prepare data for analysis
# --- PREPARE DATA --- #
# Add baselines
baselines = ds_metaecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(system_nr,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_metaecosystems %>%
left_join(baselines)
# Filter and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
metaecosystem_type %in% metaecosystem_type_selected,
#!is.na(total_water_addition_ml),
!is.na(!!sym(response_variable_selected))) %>%
mutate(metaecosystem_type = case_when(metaecosystem_type == "Small-Large unconnected" ~ "SL unc",
metaecosystem_type == "Medium-Medium unconnected" ~ "MM unc",
metaecosystem_type == "Small-Small meta-ecosystem" ~ "SS con",
metaecosystem_type == "Medium-Medium meta-ecosystem" ~ "MM con",
metaecosystem_type == "Large-Large meta-ecosystem" ~ "LL con",
metaecosystem_type == "Small-Large meta-ecosystem" ~ "SL con",
TRUE ~ metaecosystem_type),
type_conn = paste(metaecosystem_type, connection))Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI OF FILTERED DATA --- #
p1=plot.metaecos.points(data_for_analysis %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.points(data_for_analysis %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- DEFINE MODEL FORMULA --- #
formula = paste("get(response_variable_selected) ~",
"type_conn * disturbance * day +",
"(day | baseline) +",
"(day | system_nr)") %>%
print()## [1] "get(response_variable_selected) ~ type_conn * disturbance * day + (day | baseline) + (day | system_nr)"# --- FIND ECOSYSTEM COMBINATIONS FOR ANALYSIS --- #
# Set up parameters
bootstrap_iterations = 1000
# Produce random (bootstrapped) rows
random_sets_of_sets <- runif(bootstrap_iterations,
min = 1,
max = nrow(ecos_combin_unconn_sets_of_sets)) %>%
round()
# Filter combinations according to the random rows
sets_of_sets_filtered = ecos_combin_unconn_sets_of_sets %>%
filter(row_number() %in% random_sets_of_sets)# --- DEFINE WHERE TO SAVE (AND FROM WHERE TO LATER READ) THE RESULTS OF THE ANALYSIS --- #
file_path = here("3_results", "tables",
paste0("results_metaeco_",
response_variable_selected,
"_",
bootstrap_iterations,
"_iterations",
".csv"))# --- FIND MODEL STATISTICS THROUGH MULTIPLE ITERATIONS RESHUFFLING ECOSYSTEM COMBINATIONS --- #
# Set parameters and initialise
results_table = list()
failed_optimizers = 0
# Fit models
for (comb_i in 1:bootstrap_iterations) {
# Find system numbers
system_nr_unconn_selected = ecos_combin_unconn_sets %>%
filter(metaecosystem_type == "Small-Large" &
disturbance == "low" &
set == sets_of_sets_filtered[comb_i, ]$set_SL_low |
metaecosystem_type == "Small-Large" &
disturbance == "high" &
set == sets_of_sets_filtered[comb_i, ]$set_SL_high |
metaecosystem_type == "Medium-Medium" &
disturbance == "low" &
set == sets_of_sets_filtered[comb_i, ]$set_MM_low |
metaecosystem_type == "Medium-Medium" &
disturbance == "high" &
set == sets_of_sets_filtered[comb_i, ]$set_MM_high) %>%
pull(system_nr)
# Prepare data for analysis
data_for_analysis_comb_i = data_for_analysis %>%
filter(system_nr %in% system_nr_unconn_selected | connection == "connected")
# Run model
model = try.different.optimizers.metaecos(data_for_analysis_comb_i,
formula)
# If all the optimisers fail, move on to the next iteration
if (is.null(model)) {
cat("This model could not be fitted with any optimiser (",
system_nr_unconn_selected,
") \n")
failed_optimizers = failed_optimizers + 1
next
}
# Residuals - show Q-Q plot
# print(qqnorm(resid(model))); print(qqline(resid(model)))
# Residuals - save Residuals vs Fitted plot
# plot = data_for_analysis_comb_i %>%
# mutate(predicted = fitted(model),
# residuals = resid(model)) %>%
# ggplot(aes(x = predicted,
# y = residuals)) +
# geom_point()
#
# ggsave(here("3_results",
# "residual_plots",
# paste0("res_vs_fit_",
# response_variable_selected,
# "_combination_",
# comb_i,
# ".png")),
# plot = plot,
# width = 8,
# height = 6)
# Set up contrasts
emmeans_output = emmeans(model,
specs = ~ type_conn * day * disturbance ,
method = "pairwise",
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
summary(emmeans_output)
zeros = rep(0, 8)
high_MM_conn = zeros; high_MM_conn[1] = 1
high_MM_unc = zeros; high_MM_unc[2] = 1
high_SL_conn = zeros; high_SL_conn[3] = 1
high_SL_unc = zeros; high_SL_unc[4] = 1
low_MM_conn = zeros; low_MM_conn[5] = 1
low_MM_unc = zeros; low_MM_unc[6] = 1
low_SL_conn = zeros; low_SL_conn[7] = 1
low_SL_unc = zeros; low_SL_unc[8] = 1
# Save contrasts in a table
results_table[[comb_i]] = contrast(
emmeans_output,
method = list("high SL_conn - SL_unc" = high_SL_conn - high_SL_unc,
"high MM_conn - MM_unc" = high_MM_conn - high_MM_unc,
"low SL_conn - SL_unc" = low_SL_conn - low_SL_unc,
"low MM_conn - MM_unc" = low_MM_conn - low_MM_unc)) %>%
as.data.frame() %>%
mutate(combination = comb_i,
system_nr_unconnected_systems = paste(system_nr_unconn_selected, collapse = ", "))
}
# Tell how many optimizers failed
print(paste((failed_optimizers/bootstrap_iterations * 100),
"% of combinations created models that could not be fit"))
# Bind rows within the list results_table
results_table = results_table %>%
bind_rows()
# Save the results
write.csv(results_table,
file = file_path,
row.names = F)# --- READ THE RESULTS AFTER LENGTHY COMPUTATION --- #
results_table = read.csv(file = file_path)Show iterated results - p value distributions
# --- SHOW ITERATED RESULTS - P VALUE DISTRIBUTIONS --- #
results_table %>%
filter(contrast == "high MM_conn - MM_unc") %>%
pull(p.value) %>%
hist(main = "p values high MM_conn - MM_unc")
results_table %>%
filter(contrast == "high SL_conn - SL_unc") %>%
pull(p.value) %>%
hist(main = "p values high SL_conn - SL_unc")
results_table %>%
filter(contrast == "low MM_conn - MM_unc") %>%
pull(p.value) %>%
hist(main = "p values low MM_conn - MM_unc")
results_table %>%
filter(contrast == "low SL_conn - SL_unc") %>%
pull(p.value) %>%
hist(main = "p values low SL_conn vs SL_unc")
Show iterated results - table with averaged values
# --- SHOW ITERATED RESULTS - TABLE WITH AVERAGED VALUES --- #
results_table %>%
group_by(contrast) %>%
summarise(estimate = median(estimate),
SE = median(SE),
df = median(df),
t.ratio = median(t.ratio),
p.value = median(p.value)) %>%
as.data.frame() %>%
mutate(estimate = round(estimate, digits = 3),
SE = round(SE, digits = 3),
df = round(df, digits = 3),
t.ratio = round(t.ratio, digits = 3),
p.value = round(p.value, digits = 3),
evidence = "",
evidence = ifelse(p.value > 0.1,
"none",
evidence),
evidence = ifelse(p.value < 0.1,
"* weak",
evidence),
evidence = ifelse(p.value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p.value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p.value < 0.001,
"**** very strong",
evidence),
p.value = ifelse(p.value < 0.001,
"< 0.001",
p.value))## contrast estimate SE df t.ratio p.value evidence
## 1 high MM_conn - MM_unc 0.064 0.045 44.803 1.377 0.174 none
## 2 high SL_conn - SL_unc -0.124 0.036 44.803 -3.407 0.001 *** strong
## 3 low MM_conn - MM_unc 0.065 0.045 44.803 1.432 0.157 none
## 4 low SL_conn - SL_unc -0.129 0.034 44.803 -3.800 < 0.001 **** very strongGamma
response_variable_selected = "metaecosystem_richness"Plot original data - means
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
# Create plots
p1=plot.metaecos.points(ds_metaecosystems %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.points(ds_metaecosystems %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(0.9, 1),
common.legend = TRUE,
align = "v")
Plot original data - single replicates
# --- ORIGINAL DATA - PLOT SINGLE REPLICATES --- #
p1=plot.metaecos.replicates(ds_metaecosystems %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.replicates(ds_metaecosystems %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(0.9, 1),
common.legend = TRUE,
align = "v")
Prepare data for analysis
# --- PREPARE DATA --- #
# Add baselines
baselines = ds_metaecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(system_nr,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_metaecosystems %>%
left_join(baselines)
# Filter and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
metaecosystem_type %in% metaecosystem_type_selected,
#!is.na(total_water_addition_ml),
!is.na(!!sym(response_variable_selected))) %>%
mutate(metaecosystem_type = case_when(metaecosystem_type == "Small-Large unconnected" ~ "SL unc",
metaecosystem_type == "Medium-Medium unconnected" ~ "MM unc",
metaecosystem_type == "Small-Small meta-ecosystem" ~ "SS con",
metaecosystem_type == "Medium-Medium meta-ecosystem" ~ "MM con",
metaecosystem_type == "Large-Large meta-ecosystem" ~ "LL con",
metaecosystem_type == "Small-Large meta-ecosystem" ~ "SL con",
TRUE ~ metaecosystem_type),
type_conn = paste(metaecosystem_type, connection))Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI OF FILTERED DATA --- #
p1=plot.metaecos.points(data_for_analysis %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.points(data_for_analysis %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- DEFINE MODEL FORMULA --- #
formula = paste("get(response_variable_selected) ~",
"type_conn * disturbance * day +",
"(day | baseline) +",
"(day | system_nr)") %>%
print()## [1] "get(response_variable_selected) ~ type_conn * disturbance * day + (day | baseline) + (day | system_nr)"# --- FIND ECOSYSTEM COMBINATIONS FOR ANALYSIS --- #
# Set up parameters
bootstrap_iterations = 1000
# Produce random (bootstrapped) rows
random_sets_of_sets <- runif(bootstrap_iterations,
min = 1,
max = nrow(ecos_combin_unconn_sets_of_sets)) %>%
round()
# Filter combinations according to the random rows
sets_of_sets_filtered = ecos_combin_unconn_sets_of_sets %>%
filter(row_number() %in% random_sets_of_sets)# --- DEFINE WHERE TO SAVE (AND FROM WHERE TO LATER READ) THE RESULTS OF THE ANALYSIS --- #
file_path = here("3_results", "tables",
paste0("results_metaeco_",
response_variable_selected,
"_",
bootstrap_iterations,
"_iterations",
".csv"))# --- FIND MODEL STATISTICS THROUGH MULTIPLE ITERATIONS RESHUFFLING ECOSYSTEM COMBINATIONS --- #
# Set parameters and initialise
results_table = list()
failed_optimizers = 0
# Fit models
for (comb_i in 1:bootstrap_iterations) {
# Find system numbers
system_nr_unconn_selected = ecos_combin_unconn_sets %>%
filter(metaecosystem_type == "Small-Large" &
disturbance == "low" &
set == sets_of_sets_filtered[comb_i, ]$set_SL_low |
metaecosystem_type == "Small-Large" &
disturbance == "high" &
set == sets_of_sets_filtered[comb_i, ]$set_SL_high |
metaecosystem_type == "Medium-Medium" &
disturbance == "low" &
set == sets_of_sets_filtered[comb_i, ]$set_MM_low |
metaecosystem_type == "Medium-Medium" &
disturbance == "high" &
set == sets_of_sets_filtered[comb_i, ]$set_MM_high) %>%
pull(system_nr)
# Prepare data for analysis
data_for_analysis_comb_i = data_for_analysis %>%
filter(system_nr %in% system_nr_unconn_selected | connection == "connected")
# Run model
model = try.different.optimizers.metaecos(data_for_analysis_comb_i,
formula)
# If all the optimisers fail, move on to the next iteration
if (is.null(model)) {
cat("This model could not be fitted with any optimiser (",
system_nr_unconn_selected,
") \n")
failed_optimizers = failed_optimizers + 1
next
}
# Residuals - show Q-Q plot
# print(qqnorm(resid(model))); print(qqline(resid(model)))
# Residuals - save Residuals vs Fitted plot
# plot = data_for_analysis_comb_i %>%
# mutate(predicted = fitted(model),
# residuals = resid(model)) %>%
# ggplot(aes(x = predicted,
# y = residuals)) +
# geom_point()
#
# ggsave(here("3_results",
# "residual_plots",
# paste0("res_vs_fit_",
# response_variable_selected,
# "_combination_",
# comb_i,
# ".png")),
# plot = plot,
# width = 8,
# height = 6)
# Set up contrasts
emmeans_output = emmeans(model,
specs = ~ type_conn * day * disturbance ,
method = "pairwise",
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
summary(emmeans_output)
zeros = rep(0, 8)
high_MM_conn = zeros; high_MM_conn[1] = 1
high_MM_unc = zeros; high_MM_unc[2] = 1
high_SL_conn = zeros; high_SL_conn[3] = 1
high_SL_unc = zeros; high_SL_unc[4] = 1
low_MM_conn = zeros; low_MM_conn[5] = 1
low_MM_unc = zeros; low_MM_unc[6] = 1
low_SL_conn = zeros; low_SL_conn[7] = 1
low_SL_unc = zeros; low_SL_unc[8] = 1
# Save contrasts in a table
results_table[[comb_i]] = contrast(
emmeans_output,
method = list("high SL_conn - SL_unc" = high_SL_conn - high_SL_unc,
"high MM_conn - MM_unc" = high_MM_conn - high_MM_unc,
"low SL_conn - SL_unc" = low_SL_conn - low_SL_unc,
"low MM_conn - MM_unc" = low_MM_conn - low_MM_unc)) %>%
as.data.frame() %>%
mutate(combination = comb_i,
system_nr_unconnected_systems = paste(system_nr_unconn_selected, collapse = ", "))
}
# Tell how many optimizers failed
print(paste((failed_optimizers/bootstrap_iterations * 100),
"% of combinations created models that could not be fit"))
# Bind rows within the list results_table
results_table = results_table %>%
bind_rows()
# Save the results
write.csv(results_table,
file = file_path,
row.names = F)# --- READ THE RESULTS AFTER LENGTHY COMPUTATION --- #
results_table = read.csv(file = file_path)Show iterated results - p value distributions
# --- SHOW ITERATED RESULTS - P VALUE DISTRIBUTIONS --- #
results_table %>%
filter(contrast == "high MM_conn - MM_unc") %>%
pull(p.value) %>%
hist(main = "p values high MM_conn - MM_unc")
results_table %>%
filter(contrast == "high SL_conn - SL_unc") %>%
pull(p.value) %>%
hist(main = "p values high SL_conn - SL_unc")
results_table %>%
filter(contrast == "low MM_conn - MM_unc") %>%
pull(p.value) %>%
hist(main = "p values low MM_conn - MM_unc")
results_table %>%
filter(contrast == "low SL_conn - SL_unc") %>%
pull(p.value) %>%
hist(main = "p values low SL_conn vs SL_unc")
Show iterated results - table with averaged values
# --- SHOW ITERATED RESULTS - TABLE WITH AVERAGED VALUES --- #
results_table %>%
group_by(contrast) %>%
summarise(estimate = median(estimate),
SE = median(SE),
df = median(df),
t.ratio = median(t.ratio),
p.value = median(p.value)) %>%
as.data.frame() %>%
mutate(estimate = round(estimate, digits = 3),
SE = round(SE, digits = 3),
df = round(df, digits = 3),
t.ratio = round(t.ratio, digits = 3),
p.value = round(p.value, digits = 3),
evidence = "",
evidence = ifelse(p.value > 0.1,
"none",
evidence),
evidence = ifelse(p.value < 0.1,
"* weak",
evidence),
evidence = ifelse(p.value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p.value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p.value < 0.001,
"**** very strong",
evidence),
p.value = ifelse(p.value < 0.001,
"< 0.001",
p.value))## contrast estimate SE df t.ratio p.value evidence
## 1 high MM_conn - MM_unc 0.367 0.442 39.171 0.822 0.416 none
## 2 high SL_conn - SL_unc -0.339 0.353 38.411 -0.950 0.348 none
## 3 low MM_conn - MM_unc 0.967 0.445 39.581 2.173 0.036 ** moderate
## 4 low SL_conn - SL_unc 0.033 0.333 38.467 0.097 0.872 noneBiomass
response_variable_selected = "total_metaecosystem_bioarea_mm2"Plot original data - means
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
# Create plots
p1=plot.metaecos.points(ds_metaecosystems %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.points(ds_metaecosystems %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(0.9, 1),
common.legend = TRUE,
align = "v")
Plot original data - single replicates
# --- ORIGINAL DATA - PLOT SINGLE REPLICATES --- #
p1=plot.metaecos.replicates(ds_metaecosystems %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.replicates(ds_metaecosystems %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(0.9, 1),
common.legend = TRUE,
align = "v")
Prepare data for analysis
# --- PREPARE DATA --- #
# Add baselines
baselines = ds_metaecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(system_nr,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_metaecosystems %>%
left_join(baselines)
# Filter and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
metaecosystem_type %in% metaecosystem_type_selected,
#!is.na(total_water_addition_ml),
!is.na(!!sym(response_variable_selected))) %>%
mutate(metaecosystem_type = case_when(metaecosystem_type == "Small-Large unconnected" ~ "SL unc",
metaecosystem_type == "Medium-Medium unconnected" ~ "MM unc",
metaecosystem_type == "Small-Small meta-ecosystem" ~ "SS con",
metaecosystem_type == "Medium-Medium meta-ecosystem" ~ "MM con",
metaecosystem_type == "Large-Large meta-ecosystem" ~ "LL con",
metaecosystem_type == "Small-Large meta-ecosystem" ~ "SL con",
TRUE ~ metaecosystem_type),
type_conn = paste(metaecosystem_type, connection))Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI OF FILTERED DATA --- #
p1=plot.metaecos.points(data_for_analysis %>% filter(disturbance == "high"),
metaecosystem_type_selected,
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2=plot.metaecos.points(data_for_analysis %>% filter(disturbance == "low"),
metaecosystem_type_selected,
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- DEFINE MODEL FORMULA --- #
formula = paste("get(response_variable_selected) ~",
"type_conn * disturbance * day +",
"(day | baseline) +",
"(day | system_nr)") %>%
print()## [1] "get(response_variable_selected) ~ type_conn * disturbance * day + (day | baseline) + (day | system_nr)"# --- FIND ECOSYSTEM COMBINATIONS FOR ANALYSIS --- #
# Set up parameters
bootstrap_iterations = 1000
# Produce random (bootstrapped) rows
random_sets_of_sets <- runif(bootstrap_iterations,
min = 1,
max = nrow(ecos_combin_unconn_sets_of_sets)) %>%
round()
# Filter combinations according to the random rows
sets_of_sets_filtered = ecos_combin_unconn_sets_of_sets %>%
filter(row_number() %in% random_sets_of_sets)# --- DEFINE WHERE TO SAVE (AND FROM WHERE TO LATER READ) THE RESULTS OF THE ANALYSIS --- #
file_path = here("3_results", "tables",
paste0("results_metaeco_",
response_variable_selected,
"_",
bootstrap_iterations,
"_iterations",
".csv"))# --- FIND MODEL STATISTICS THROUGH MULTIPLE ITERATIONS RESHUFFLING ECOSYSTEM COMBINATIONS --- #
# Set parameters and initialise
results_table = list()
failed_optimizers = 0
# Fit models
for (comb_i in 1:bootstrap_iterations) {
# Find system numbers
system_nr_unconn_selected = ecos_combin_unconn_sets %>%
filter(metaecosystem_type == "Small-Large" &
disturbance == "low" &
set == sets_of_sets_filtered[comb_i, ]$set_SL_low |
metaecosystem_type == "Small-Large" &
disturbance == "high" &
set == sets_of_sets_filtered[comb_i, ]$set_SL_high |
metaecosystem_type == "Medium-Medium" &
disturbance == "low" &
set == sets_of_sets_filtered[comb_i, ]$set_MM_low |
metaecosystem_type == "Medium-Medium" &
disturbance == "high" &
set == sets_of_sets_filtered[comb_i, ]$set_MM_high) %>%
pull(system_nr)
# Prepare data for analysis
data_for_analysis_comb_i = data_for_analysis %>%
filter(system_nr %in% system_nr_unconn_selected | connection == "connected")
# Run model
model = try.different.optimizers.metaecos(data_for_analysis_comb_i,
formula)
# If all the optimisers fail, move on to the next iteration
if (is.null(model)) {
cat("This model could not be fitted with any optimiser (",
system_nr_unconn_selected,
") \n")
failed_optimizers = failed_optimizers + 1
next
}
# Residuals - show Q-Q plot
# print(qqnorm(resid(model))); print(qqline(resid(model)))
# Residuals - save Residuals vs Fitted plot
# plot = data_for_analysis_comb_i %>%
# mutate(predicted = fitted(model),
# residuals = resid(model)) %>%
# ggplot(aes(x = predicted,
# y = residuals)) +
# geom_point()
#
# ggsave(here("3_results",
# "residual_plots",
# paste0("res_vs_fit_",
# response_variable_selected,
# "_combination_",
# comb_i,
# ".png")),
# plot = plot,
# width = 8,
# height = 6)
# Set up contrasts
emmeans_output = emmeans(model,
specs = ~ type_conn * day * disturbance ,
method = "pairwise",
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
summary(emmeans_output)
zeros = rep(0, 8)
high_MM_conn = zeros; high_MM_conn[1] = 1
high_MM_unc = zeros; high_MM_unc[2] = 1
high_SL_conn = zeros; high_SL_conn[3] = 1
high_SL_unc = zeros; high_SL_unc[4] = 1
low_MM_conn = zeros; low_MM_conn[5] = 1
low_MM_unc = zeros; low_MM_unc[6] = 1
low_SL_conn = zeros; low_SL_conn[7] = 1
low_SL_unc = zeros; low_SL_unc[8] = 1
# Save contrasts in a table
results_table[[comb_i]] = contrast(
emmeans_output,
method = list("high SL_conn - SL_unc" = high_SL_conn - high_SL_unc,
"high MM_conn - MM_unc" = high_MM_conn - high_MM_unc,
"low SL_conn - SL_unc" = low_SL_conn - low_SL_unc,
"low MM_conn - MM_unc" = low_MM_conn - low_MM_unc)) %>%
as.data.frame() %>%
mutate(combination = comb_i,
system_nr_unconnected_systems = paste(system_nr_unconn_selected, collapse = ", "))
}
# Tell how many optimizers failed
print(paste((failed_optimizers/bootstrap_iterations * 100),
"% of combinations created models that could not be fit"))
# Bind rows within the list results_table
results_table = results_table %>%
bind_rows()
# Save the results
write.csv(results_table,
file = file_path,
row.names = F)# --- READ THE RESULTS AFTER LENGTHY COMPUTATION --- #
results_table = read.csv(file = file_path)Show iterated results - p value distributions
# --- SHOW ITERATED RESULTS - P VALUE DISTRIBUTIONS --- #
results_table %>%
filter(contrast == "high MM_conn - MM_unc") %>%
pull(p.value) %>%
hist(main = "p values high MM_conn - MM_unc")
results_table %>%
filter(contrast == "high SL_conn - SL_unc") %>%
pull(p.value) %>%
hist(main = "p values high SL_conn - SL_unc")
results_table %>%
filter(contrast == "low MM_conn - MM_unc") %>%
pull(p.value) %>%
hist(main = "p values low MM_conn - MM_unc")
results_table %>%
filter(contrast == "low SL_conn - SL_unc") %>%
pull(p.value) %>%
hist(main = "p values low SL_conn vs SL_unc")
Show iterated results - table with averaged values
# --- SHOW ITERATED RESULTS - TABLE WITH AVERAGED VALUES --- #
results_table %>%
group_by(contrast) %>%
summarise(estimate = median(estimate),
SE = median(SE),
df = median(df),
t.ratio = median(t.ratio),
p.value = median(p.value)) %>%
as.data.frame() %>%
mutate(estimate = round(estimate, digits = 3),
SE = round(SE, digits = 3),
df = round(df, digits = 3),
t.ratio = round(t.ratio, digits = 3),
p.value = round(p.value, digits = 3),
evidence = "",
evidence = ifelse(p.value > 0.1,
"none",
evidence),
evidence = ifelse(p.value < 0.1,
"* weak",
evidence),
evidence = ifelse(p.value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p.value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p.value < 0.001,
"**** very strong",
evidence),
p.value = ifelse(p.value < 0.001,
"< 0.001",
p.value))## contrast estimate SE df t.ratio p.value evidence
## 1 high MM_conn - MM_unc 17.326 13.109 33.314 1.325 0.194 none
## 2 high SL_conn - SL_unc -32.905 10.510 33.314 -3.106 0.004 *** strong
## 3 low MM_conn - MM_unc 38.759 13.109 33.314 2.945 0.006 *** strong
## 4 low SL_conn - SL_unc -21.448 9.909 33.314 -2.164 0.038 ** moderateEcosystems
Shannon
response_variable_selected = "shannon"Plot original data
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp2 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Prepare data for analysis
# --- PREPARE DATA FOR ANALYSIS --- #
# Add baselines
baselines = ds_ecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(culture_ID,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_ecosystems %>%
left_join(baselines)
# Filter data and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
#!is.na(water_addition_ml),
!is.na(!!sym(response_variable_selected)),
!is.infinite(!!sym(response_variable_selected))) %>%
mutate(ecosystem_type = case_when(ecosystem_type == "Small unconnected" ~ "S",
ecosystem_type == "Medium unconnected" ~ "M",
ecosystem_type == "Large unconnected" ~ "L",
ecosystem_type == "Small connected to small" ~ "S_S",
ecosystem_type == "Small connected to large" ~ "S_L",
ecosystem_type == "Medium connected to medium" ~ "M_M",
ecosystem_type == "Large connected to large" ~ "L_L",
ecosystem_type == "Large connected to small" ~ "L_S",
TRUE ~ ecosystem_type)) %>%
ungroup()Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp2 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- MODEL - CONSTRUCT --- #
# Define formula
formula = paste("get(response_variable_selected) ~",
"ecosystem_type * disturbance * day +",
"(day | culture_ID) +",
"(day | baseline)") %>%
print()## [1] "get(response_variable_selected) ~ ecosystem_type * disturbance * day + (day | culture_ID) + (day | baseline)"# Construct model
model = try.different.optimizers.ecos(data_for_analysis,
formula)## [1] "Model successfully fitted with optimizer: bobyqa "# --- MODEL - SUMMARY --- #
print(summary(model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
## Control: lmerControl(optimizer = opt$optimizer)
##
## AIC BIC logLik deviance df.resid
## 352.4 526.4 -137.2 274.4 602
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5 -0.6 0.0 0.7 3.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0e+00 0e+00
## day 1e-18 1e-09 NaN
## baseline (Intercept) 4e-11 7e-06
## day 2e-13 5e-07 -1.00
## Residual 9e-02 3e-01
## Number of obs: 641, groups: culture_ID, 109; baseline, 109
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1e+00 2e-01 6e+02 9.6
## ecosystem_typeL_L -6e-02 2e-01 6e+02 -0.3
## ecosystem_typeL_S -3e-01 2e-01 6e+02 -1.2
## ecosystem_typeM -3e-01 2e-01 6e+02 -1.3
## ecosystem_typeM_M -6e-02 2e-01 6e+02 -0.3
## ecosystem_typeS 1e-01 2e-01 6e+02 0.6
## ecosystem_typeS_L 5e-01 2e-01 6e+02 2.5
## ecosystem_typeS_S 3e-01 2e-01 6e+02 1.7
## disturbancelow 4e-01 2e-01 6e+02 1.6
## day -7e-04 8e-03 6e+02 -0.1
## ecosystem_typeL_L:disturbancelow -3e-01 3e-01 6e+02 -1.0
## ecosystem_typeL_S:disturbancelow -3e-01 3e-01 6e+02 -0.9
## ecosystem_typeM:disturbancelow -4e-01 3e-01 6e+02 -1.2
## ecosystem_typeM_M:disturbancelow -4e-01 3e-01 6e+02 -1.5
## ecosystem_typeS:disturbancelow -2e-01 3e-01 6e+02 -0.6
## ecosystem_typeS_L:disturbancelow -1e+00 3e-01 6e+02 -3.2
## ecosystem_typeS_S:disturbancelow -4e-01 3e-01 6e+02 -1.6
## ecosystem_typeL_L:day 2e-03 1e-02 6e+02 0.2
## ecosystem_typeL_S:day 8e-03 1e-02 6e+02 0.7
## ecosystem_typeM:day 3e-03 1e-02 6e+02 0.3
## ecosystem_typeM_M:day -2e-03 1e-02 6e+02 -0.2
## ecosystem_typeS:day -7e-02 1e-02 6e+02 -5.1
## ecosystem_typeS_L:day -6e-02 1e-02 6e+02 -5.7
## ecosystem_typeS_S:day -7e-02 1e-02 6e+02 -6.5
## disturbancelow:day -2e-02 1e-02 6e+02 -1.4
## ecosystem_typeL_L:disturbancelow:day 1e-02 1e-02 6e+02 0.8
## ecosystem_typeL_S:disturbancelow:day 2e-02 2e-02 6e+02 1.0
## ecosystem_typeM:disturbancelow:day 2e-02 2e-02 6e+02 1.4
## ecosystem_typeM_M:disturbancelow:day 2e-02 1e-02 6e+02 1.3
## ecosystem_typeS:disturbancelow:day 4e-02 2e-02 6e+02 2.3
## ecosystem_typeS_L:disturbancelow:day 8e-02 2e-02 6e+02 5.0
## ecosystem_typeS_S:disturbancelow:day 4e-02 1e-02 6e+02 2.8
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ecosystem_typeL_L 0.732
## ecosystem_typeL_S 0.240
## ecosystem_typeM 0.205
## ecosystem_typeM_M 0.741
## ecosystem_typeS 0.579
## ecosystem_typeS_L 0.014 *
## ecosystem_typeS_S 0.086 .
## disturbancelow 0.109
## day 0.930
## ecosystem_typeL_L:disturbancelow 0.318
## ecosystem_typeL_S:disturbancelow 0.361
## ecosystem_typeM:disturbancelow 0.228
## ecosystem_typeM_M:disturbancelow 0.128
## ecosystem_typeS:disturbancelow 0.555
## ecosystem_typeS_L:disturbancelow 0.002 **
## ecosystem_typeS_S:disturbancelow 0.119
## ecosystem_typeL_L:day 0.810
## ecosystem_typeL_S:day 0.485
## ecosystem_typeM:day 0.781
## ecosystem_typeM_M:day 0.836
## ecosystem_typeS:day 4e-07 ***
## ecosystem_typeS_L:day 2e-08 ***
## ecosystem_typeS_S:day 2e-10 ***
## disturbancelow:day 0.149
## ecosystem_typeL_L:disturbancelow:day 0.429
## ecosystem_typeL_S:disturbancelow:day 0.295
## ecosystem_typeM:disturbancelow:day 0.155
## ecosystem_typeM_M:disturbancelow:day 0.196
## ecosystem_typeS:disturbancelow:day 0.022 *
## ecosystem_typeS_L:disturbancelow:day 6e-07 ***
## ecosystem_typeS_S:disturbancelow:day 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# --- MODEL - ANOVA --- #
car::Anova(model, type = "III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: get(response_variable_selected)
## Chisq Df Pr(>Chisq)
## (Intercept) 92.1969 1 < 2.2e-16 ***
## ecosystem_type 26.7429 7 0.0003707 ***
## disturbance 2.5795 1 0.1082569
## day 0.0077 1 0.9302887
## ecosystem_type:disturbance 11.9786 7 0.1012636
## ecosystem_type:day 155.7995 7 < 2.2e-16 ***
## disturbance:day 2.0906 1 0.1482065
## ecosystem_type:disturbance:day 37.7566 7 3.371e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# --- ESTIMATE MARGINAL MEANS --- #
emmeans_output = emmeans(model,
specs = ~ ecosystem_type * disturbance * day,
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
emmeans_output## ecosystem_type disturbance day emmean SE df lower.CL upper.CL
## L high 17.8 1.468 0.0547 641 1.306 1.630
## L_L high 17.8 1.446 0.0387 641 1.331 1.560
## L_S high 17.8 1.353 0.0547 641 1.191 1.515
## M high 17.8 1.248 0.0547 641 1.086 1.410
## M_M high 17.8 1.370 0.0387 641 1.255 1.484
## S high 17.8 0.336 0.0736 641 0.118 0.554
## S_L high 17.8 0.854 0.0547 641 0.692 1.016
## S_S high 17.8 0.621 0.0412 641 0.499 0.743
## L low 17.8 1.526 0.0547 641 1.364 1.688
## L_L low 17.8 1.433 0.0387 641 1.318 1.547
## L_S low 17.8 1.429 0.0547 641 1.267 1.591
## M low 17.8 1.341 0.0547 641 1.179 1.503
## M_M low 17.8 1.341 0.0387 641 1.226 1.455
## S low 17.8 0.939 0.0557 641 0.774 1.104
## S_L low 17.8 1.373 0.0547 641 1.211 1.535
## S_S low 17.8 0.966 0.0387 641 0.852 1.081
##
## Degrees-of-freedom method: satterthwaite
## Results are given on the get (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 16 estimates# --- CODE EACH LEVEL OF DISTURBANCE AND ECOSYSTEM TYPE TO THEN PRODUCE CONTRASTS --- #
high_L = c(1, rep(0,15))
high_L_L = c(rep(0,1), 1, rep(0,14))
high_L_S = c(rep(0,2), 1, rep(0,13))
high_M = c(rep(0,3), 1, rep(0,12))
high_M_M = c(rep(0,4), 1, rep(0,11))
high_S = c(rep(0,5), 1, rep(0,10))
high_S_L = c(rep(0,6), 1, rep(0,9))
high_S_S = c(rep(0,7), 1, rep(0,8))
low_L = c(rep(0,8), 1, rep(0,7))
low_L_L = c(rep(0,9), 1, rep(0,6))
low_L_S = c(rep(0,10), 1, rep(0,5))
low_M = c(rep(0,11), 1, rep(0,4))
low_M_M = c(rep(0,12), 1, rep(0,3))
low_S = c(rep(0,13), 1, rep(0,2))
low_S_L = c(rep(0,14), 1, rep(0,1))
low_S_S = c(rep(0,15), 1)# --- PRODUCE ECOSYSTEM TYPE CONTRASTS --- #
# Set parameters
n_of_digits = 3
# Compute constrasts
contrasts = contrast(emmeans_output,
method = list("high S_L - S" = high_S_L - high_S,
"high S_L - S_S" = high_S_L - high_S_S,
"high S_S - S" = high_S_S - high_S,
"high M_M - M" = high_M_M - high_M,
"high L_S - L" = high_L_S - high_L,
"high L_S - L_L" = high_L_S - high_L_L,
"high L_L - L" = high_L_L - high_L,
"low S_L - S" = low_S_L - low_S,
"low S_L - S_S" = low_S_L - low_S_S,
"low S_S - S" = low_S_S - low_S,
"low M_M - M" = low_M_M - low_M,
"low L_S - L" = low_L_S - low_L,
"low L_S - L_L" = low_L_S - low_L_L,
"low L_L - L" = low_L_L - low_L)) %>%
as.data.frame() %>%
mutate(p.value = round(p.value, digits = n_of_digits),
estimate = round(estimate, digits = n_of_digits),
SE = round(SE, digits = n_of_digits),
df = round(df, digits = n_of_digits),
t.ratio = round(t.ratio, digits = n_of_digits),
e = "",
e = ifelse(p.value > 0.1,
"",
e),
e = ifelse(p.value < 0.05,
"*",
e),
e = ifelse(p.value < 0.01,
"**",
e),
e = ifelse(p.value < 0.001,
"***",
e)) %>%
rename(" " = e)Show results
# --- SHOW ECOSYSTEM TYPE CONSTRASTS --- #
contrasts## contrast estimate SE df t.ratio p.value
## 1 high S_L - S 0.518 0.092 641 5.647 0.000 ***
## 2 high S_L - S_S 0.233 0.068 641 3.399 0.001 **
## 3 high S_S - S 0.285 0.084 641 3.382 0.001 **
## 4 high M_M - M 0.122 0.067 641 1.817 0.070
## 5 high L_S - L -0.115 0.077 641 -1.490 0.137
## 6 high L_S - L_L -0.093 0.067 641 -1.384 0.167
## 7 high L_L - L -0.023 0.067 641 -0.337 0.736
## 8 low S_L - S 0.434 0.078 641 5.562 0.000 ***
## 9 low S_L - S_S 0.407 0.067 641 6.071 0.000 ***
## 10 low S_S - S 0.027 0.068 641 0.402 0.688
## 11 low M_M - M -0.001 0.067 641 -0.009 0.993
## 12 low L_S - L -0.098 0.077 641 -1.261 0.208
## 13 low L_S - L_L -0.004 0.067 641 -0.060 0.952
## 14 low L_L - L -0.094 0.067 641 -1.395 0.163Show model residuals residuals
# --- PLOT MODEL RESIDUALS --- #
# Simulate residuals from the fitted model
simulationOutput <- simulateResiduals(fittedModel = model,
n = 1000)
# Test for overdispersion in the model to check if variance exceeds mean
testDispersion(simulationOutput)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.0037, p-value = 0.948
## alternative hypothesis: two.sided# Plot Q-Q and Residuals vs Fitted plots
plot(simulationOutput)
Biomass
response_variable_selected = "bioarea_mm2_per_ml"Plot original data
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Prepare data for analysis
# --- PREPARE DATA FOR ANALYSIS --- #
# Add baselines
baselines = ds_ecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(culture_ID,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_ecosystems %>%
left_join(baselines)
# Filter data and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
#!is.na(water_addition_ml),
!is.na(!!sym(response_variable_selected)),
!is.infinite(!!sym(response_variable_selected))) %>%
mutate(ecosystem_type = case_when(ecosystem_type == "Small unconnected" ~ "S",
ecosystem_type == "Medium unconnected" ~ "M",
ecosystem_type == "Large unconnected" ~ "L",
ecosystem_type == "Small connected to small" ~ "S_S",
ecosystem_type == "Small connected to large" ~ "S_L",
ecosystem_type == "Medium connected to medium" ~ "M_M",
ecosystem_type == "Large connected to large" ~ "L_L",
ecosystem_type == "Large connected to small" ~ "L_S",
TRUE ~ ecosystem_type)) %>%
ungroup()Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- MODEL - CONSTRUCT --- #
# Define formula
formula = paste("get(response_variable_selected) ~",
"ecosystem_type * disturbance * day +",
"(day | culture_ID) +",
"(day | baseline)") %>%
print()## [1] "get(response_variable_selected) ~ ecosystem_type * disturbance * day + (day | culture_ID) + (day | baseline)"# Construct model
model = try.different.optimizers.ecos(data_for_analysis,
formula)## [1] "Model successfully fitted with optimizer: bobyqa "# --- MODEL - SUMMARY --- #
print(summary(model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
## Control: lmerControl(optimizer = opt$optimizer)
##
## AIC BIC logLik deviance df.resid
## 1639.4 1814.3 -780.7 1561.4 615
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1 -0.6 0.0 0.5 3.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-01 0.39
## day 2e-04 0.01 -1.00
## baseline (Intercept) 1e-01 0.34
## day 2e-04 0.01 -1.00
## Residual 6e-01 0.77
## Number of obs: 654, groups: culture_ID, 109; baseline, 109
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.94 0.46 122.57 12.9
## ecosystem_typeL_L -0.15 0.56 122.57 -0.3
## ecosystem_typeL_S -1.18 0.65 122.57 -1.8
## ecosystem_typeM -1.63 0.65 122.57 -2.5
## ecosystem_typeM_M -0.26 0.56 122.57 -0.5
## ecosystem_typeS -4.14 0.69 122.57 -6.0
## ecosystem_typeS_L -2.90 0.65 122.57 -4.5
## ecosystem_typeS_S -3.72 0.56 122.57 -6.6
## disturbancelow -0.99 0.65 122.57 -1.5
## day -0.16 0.02 167.67 -7.2
## ecosystem_typeL_L:disturbancelow 0.68 0.80 122.57 0.9
## ecosystem_typeL_S:disturbancelow 1.68 0.92 122.57 1.8
## ecosystem_typeM:disturbancelow 1.80 0.92 122.57 2.0
## ecosystem_typeM_M:disturbancelow 1.35 0.80 122.57 1.7
## ecosystem_typeS:disturbancelow 3.12 0.95 122.57 3.3
## ecosystem_typeS_L:disturbancelow 1.90 0.92 122.57 2.1
## ecosystem_typeS_S:disturbancelow 2.37 0.80 122.57 3.0
## ecosystem_typeL_L:day -0.01 0.03 167.67 -0.3
## ecosystem_typeL_S:day 0.01 0.03 167.67 0.4
## ecosystem_typeM:day 0.02 0.03 167.67 0.7
## ecosystem_typeM_M:day -0.03 0.03 167.67 -1.1
## ecosystem_typeS:day 0.09 0.03 167.67 2.5
## ecosystem_typeS_L:day 0.04 0.03 167.67 1.3
## ecosystem_typeS_S:day 0.07 0.03 167.67 2.7
## disturbancelow:day 0.07 0.03 167.67 2.2
## ecosystem_typeL_L:disturbancelow:day -0.05 0.04 167.67 -1.3
## ecosystem_typeL_S:disturbancelow:day -0.08 0.04 167.67 -1.7
## ecosystem_typeM:disturbancelow:day -0.10 0.04 167.67 -2.3
## ecosystem_typeM_M:disturbancelow:day -0.05 0.04 167.67 -1.4
## ecosystem_typeS:disturbancelow:day -0.15 0.05 167.67 -3.2
## ecosystem_typeS_L:disturbancelow:day -0.08 0.04 167.67 -1.8
## ecosystem_typeS_S:disturbancelow:day -0.12 0.04 167.67 -3.1
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ecosystem_typeL_L 0.789
## ecosystem_typeL_S 0.073 .
## ecosystem_typeM 0.013 *
## ecosystem_typeM_M 0.643
## ecosystem_typeS 2e-08 ***
## ecosystem_typeS_L 2e-05 ***
## ecosystem_typeS_S 1e-09 ***
## disturbancelow 0.130
## day 2e-11 ***
## ecosystem_typeL_L:disturbancelow 0.395
## ecosystem_typeL_S:disturbancelow 0.070 .
## ecosystem_typeM:disturbancelow 0.052 .
## ecosystem_typeM_M:disturbancelow 0.093 .
## ecosystem_typeS:disturbancelow 0.001 **
## ecosystem_typeS_L:disturbancelow 0.041 *
## ecosystem_typeS_S:disturbancelow 0.004 **
## ecosystem_typeL_L:day 0.728
## ecosystem_typeL_S:day 0.695
## ecosystem_typeM:day 0.474
## ecosystem_typeM_M:day 0.258
## ecosystem_typeS:day 0.012 *
## ecosystem_typeS_L:day 0.203
## ecosystem_typeS_S:day 0.009 **
## disturbancelow:day 0.026 *
## ecosystem_typeL_L:disturbancelow:day 0.213
## ecosystem_typeL_S:disturbancelow:day 0.086 .
## ecosystem_typeM:disturbancelow:day 0.021 *
## ecosystem_typeM_M:disturbancelow:day 0.163
## ecosystem_typeS:disturbancelow:day 0.002 **
## ecosystem_typeS_L:disturbancelow:day 0.081 .
## ecosystem_typeS_S:disturbancelow:day 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# --- MODEL - ANOVA --- #
car::Anova(model, type = "III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: get(response_variable_selected)
## Chisq Df Pr(>Chisq)
## (Intercept) 166.6820 1 < 2.2e-16 ***
## ecosystem_type 124.6134 7 < 2.2e-16 ***
## disturbance 2.3252 1 0.12729
## day 51.2279 1 8.224e-13 ***
## ecosystem_type:disturbance 18.2609 7 0.01085 *
## ecosystem_type:day 33.5306 7 2.108e-05 ***
## disturbance:day 5.0218 1 0.02503 *
## ecosystem_type:disturbance:day 17.2032 7 0.01613 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# --- ESTIMATE MARGINAL MEANS --- #
emmeans_output = emmeans(model,
specs = ~ ecosystem_type * disturbance * day,
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
emmeans_output## ecosystem_type disturbance day emmean SE df lower.CL upper.CL
## L high 18 3.061 0.161 127 2.576 3.547
## L_L high 18 2.739 0.114 127 2.396 3.082
## L_S high 18 2.108 0.161 127 1.623 2.593
## M high 18 1.838 0.161 127 1.352 2.323
## M_M high 18 2.241 0.114 127 1.898 2.584
## S high 18 0.448 0.181 127 -0.094 0.991
## S_L high 18 0.891 0.161 127 0.406 1.376
## S_S high 18 0.647 0.114 127 0.304 0.990
## L low 18 3.343 0.161 127 2.858 3.828
## L_L low 18 2.831 0.114 127 2.488 3.174
## L_S low 18 2.682 0.161 127 2.196 3.167
## M low 18 2.057 0.161 127 1.572 2.543
## M_M low 18 2.898 0.114 127 2.555 3.242
## S low 18 1.214 0.161 127 0.728 1.699
## S_L low 18 1.662 0.161 127 1.176 2.147
## S_S low 18 1.154 0.114 127 0.811 1.497
##
## Degrees-of-freedom method: satterthwaite
## Results are given on the get (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 16 estimates# --- CODE EACH LEVEL OF DISTURBANCE AND ECOSYSTEM TYPE TO THEN PRODUCE CONTRASTS --- #
high_L = c(1, rep(0,15))
high_L_L = c(rep(0,1), 1, rep(0,14))
high_L_S = c(rep(0,2), 1, rep(0,13))
high_M = c(rep(0,3), 1, rep(0,12))
high_M_M = c(rep(0,4), 1, rep(0,11))
high_S = c(rep(0,5), 1, rep(0,10))
high_S_L = c(rep(0,6), 1, rep(0,9))
high_S_S = c(rep(0,7), 1, rep(0,8))
low_L = c(rep(0,8), 1, rep(0,7))
low_L_L = c(rep(0,9), 1, rep(0,6))
low_L_S = c(rep(0,10), 1, rep(0,5))
low_M = c(rep(0,11), 1, rep(0,4))
low_M_M = c(rep(0,12), 1, rep(0,3))
low_S = c(rep(0,13), 1, rep(0,2))
low_S_L = c(rep(0,14), 1, rep(0,1))
low_S_S = c(rep(0,15), 1)# --- PRODUCE ECOSYSTEM TYPE CONTRASTS --- #
# Set parameters
n_of_digits = 3
# Compute constrasts
contrasts = contrast(emmeans_output,
method = list("high S_L - S" = high_S_L - high_S,
"high S_L - S_S" = high_S_L - high_S_S,
"high S_S - S" = high_S_S - high_S,
"high M_M - M" = high_M_M - high_M,
"high L_S - L" = high_L_S - high_L,
"high L_S - L_L" = high_L_S - high_L_L,
"high L_L - L" = high_L_L - high_L,
"low S_L - S" = low_S_L - low_S,
"low S_L - S_S" = low_S_L - low_S_S,
"low S_S - S" = low_S_S - low_S,
"low M_M - M" = low_M_M - low_M,
"low L_S - L" = low_L_S - low_L,
"low L_S - L_L" = low_L_S - low_L_L,
"low L_L - L" = low_L_L - low_L)) %>%
as.data.frame() %>%
mutate(p.value = round(p.value, digits = n_of_digits),
estimate = round(estimate, digits = n_of_digits),
SE = round(SE, digits = n_of_digits),
df = round(df, digits = n_of_digits),
t.ratio = round(t.ratio, digits = n_of_digits),
e = "",
e = ifelse(p.value > 0.1,
"",
e),
e = ifelse(p.value < 0.05,
"*",
e),
e = ifelse(p.value < 0.01,
"**",
e),
e = ifelse(p.value < 0.001,
"***",
e)) %>%
rename(" " = e)Show results
# --- SHOW ECOSYSTEM TYPE CONSTRASTS --- #
contrasts## contrast estimate SE df t.ratio p.value
## 1 high S_L - S 0.442 0.242 126.838 1.826 0.070
## 2 high S_L - S_S 0.244 0.198 126.838 1.233 0.220
## 3 high S_S - S 0.199 0.214 126.838 0.930 0.354
## 4 high M_M - M 0.404 0.198 126.838 2.041 0.043 *
## 5 high L_S - L -0.953 0.228 126.838 -4.175 0.000 ***
## 6 high L_S - L_L -0.631 0.198 126.838 -3.190 0.002 **
## 7 high L_L - L -0.322 0.198 126.838 -1.631 0.105
## 8 low S_L - S 0.448 0.228 126.838 1.962 0.052
## 9 low S_L - S_S 0.507 0.198 126.838 2.565 0.011 *
## 10 low S_S - S -0.059 0.198 126.838 -0.300 0.765
## 11 low M_M - M 0.841 0.198 126.838 4.253 0.000 ***
## 12 low L_S - L -0.662 0.228 126.838 -2.897 0.004 **
## 13 low L_S - L_L -0.149 0.198 126.838 -0.755 0.452
## 14 low L_L - L -0.512 0.198 126.838 -2.591 0.011 *Show model residuals residuals
# --- PLOT MODEL RESIDUALS --- #
# Simulate residuals from the fitted model
simulationOutput <- simulateResiduals(fittedModel = model,
n = 1000)
# Test for overdispersion in the model to check if variance exceeds mean
testDispersion(simulationOutput)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.0057, p-value = 0.894
## alternative hypothesis: two.sided# Plot Q-Q and Residuals vs Fitted plots
plot(simulationOutput)
Richness
response_variable_selected = "species_richness"Plot original data
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Prepare data for analysis
# --- PREPARE DATA FOR ANALYSIS --- #
# Add baselines
baselines = ds_ecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(culture_ID,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_ecosystems %>%
left_join(baselines)
# Filter data and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
#!is.na(water_addition_ml),
!is.na(!!sym(response_variable_selected)),
!is.infinite(!!sym(response_variable_selected))) %>%
mutate(ecosystem_type = case_when(ecosystem_type == "Small unconnected" ~ "S",
ecosystem_type == "Medium unconnected" ~ "M",
ecosystem_type == "Large unconnected" ~ "L",
ecosystem_type == "Small connected to small" ~ "S_S",
ecosystem_type == "Small connected to large" ~ "S_L",
ecosystem_type == "Medium connected to medium" ~ "M_M",
ecosystem_type == "Large connected to large" ~ "L_L",
ecosystem_type == "Large connected to small" ~ "L_S",
TRUE ~ ecosystem_type)) %>%
ungroup()Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text
p2 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- MODEL - CONSTRUCT --- #
# Define formula
formula = paste("get(response_variable_selected) ~",
"ecosystem_type * disturbance * day +",
"(day | culture_ID) +",
"(day | baseline)") %>%
print()## [1] "get(response_variable_selected) ~ ecosystem_type * disturbance * day + (day | culture_ID) + (day | baseline)"# Construct model
model = try.different.optimizers.ecos(data_for_analysis,
formula)## [1] "Model successfully fitted with optimizer: bobyqa "# --- MODEL - SUMMARY --- #
print(summary(model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
## Control: lmerControl(optimizer = opt$optimizer)
##
## AIC BIC logLik deviance df.resid
## 2273.8 2448.7 -1097.9 2195.8 615
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6 -0.6 0.0 0.6 3.2
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 7e-05 0.008
## day 9e-06 0.003 -1.00
## baseline (Intercept) 1e-01 0.321
## day 7e-05 0.009 -1.00
## Residual 2e+00 1.289
## Number of obs: 654, groups: culture_ID, 109; baseline, 8
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 7e+00 7e-01 3e+02 10.5
## ecosystem_typeL_L 3e-01 8e-01 5e+02 0.4
## ecosystem_typeL_S -8e-01 9e-01 5e+02 -0.9
## ecosystem_typeM 3e-01 9e-01 5e+02 0.4
## ecosystem_typeM_M 5e-01 8e-01 5e+02 0.6
## ecosystem_typeS -1e+00 1e+00 5e+02 -1.2
## ecosystem_typeS_L 1e+00 9e-01 5e+02 1.0
## ecosystem_typeS_S -9e-01 8e-01 5e+02 -1.1
## disturbancelow 5e-01 9e-01 5e+02 0.6
## day -2e-02 3e-02 5e+02 -0.5
## ecosystem_typeL_L:disturbancelow -8e-01 1e+00 5e+02 -0.7
## ecosystem_typeL_S:disturbancelow -5e-01 1e+00 5e+02 -0.4
## ecosystem_typeM:disturbancelow -6e-01 1e+00 5e+02 -0.5
## ecosystem_typeM_M:disturbancelow -5e-01 1e+00 5e+02 -0.5
## ecosystem_typeS:disturbancelow 2e+00 1e+00 5e+02 1.5
## ecosystem_typeS_L:disturbancelow -1e+00 1e+00 5e+02 -1.1
## ecosystem_typeS_S:disturbancelow 1e+00 1e+00 5e+02 0.8
## ecosystem_typeL_L:day -4e-02 4e-02 6e+02 -1.0
## ecosystem_typeL_S:day -4e-03 5e-02 6e+02 -0.1
## ecosystem_typeM:day -9e-02 5e-02 6e+02 -1.8
## ecosystem_typeM_M:day -8e-02 4e-02 6e+02 -1.9
## ecosystem_typeS:day -2e-01 5e-02 6e+02 -4.1
## ecosystem_typeS_L:day -2e-01 5e-02 6e+02 -5.1
## ecosystem_typeS_S:day -2e-01 4e-02 6e+02 -4.5
## disturbancelow:day -7e-03 5e-02 6e+02 -0.1
## ecosystem_typeL_L:disturbancelow:day 2e-02 6e-02 6e+02 0.3
## ecosystem_typeL_S:disturbancelow:day 3e-02 7e-02 6e+02 0.5
## ecosystem_typeM:disturbancelow:day 3e-02 7e-02 6e+02 0.4
## ecosystem_typeM_M:disturbancelow:day 4e-02 6e-02 6e+02 0.6
## ecosystem_typeS:disturbancelow:day -2e-02 7e-02 6e+02 -0.3
## ecosystem_typeS_L:disturbancelow:day 2e-01 7e-02 6e+02 2.5
## ecosystem_typeS_S:disturbancelow:day 7e-04 6e-02 6e+02 0.0
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ecosystem_typeL_L 0.69
## ecosystem_typeL_S 0.37
## ecosystem_typeM 0.72
## ecosystem_typeM_M 0.53
## ecosystem_typeS 0.23
## ecosystem_typeS_L 0.31
## ecosystem_typeS_S 0.27
## disturbancelow 0.57
## day 0.59
## ecosystem_typeL_L:disturbancelow 0.49
## ecosystem_typeL_S:disturbancelow 0.69
## ecosystem_typeM:disturbancelow 0.64
## ecosystem_typeM_M:disturbancelow 0.65
## ecosystem_typeS:disturbancelow 0.15
## ecosystem_typeS_L:disturbancelow 0.27
## ecosystem_typeS_S:disturbancelow 0.40
## ecosystem_typeL_L:day 0.31
## ecosystem_typeL_S:day 0.93
## ecosystem_typeM:day 0.07 .
## ecosystem_typeM_M:day 0.06 .
## ecosystem_typeS:day 4e-05 ***
## ecosystem_typeS_L:day 5e-07 ***
## ecosystem_typeS_S:day 7e-06 ***
## disturbancelow:day 0.89
## ecosystem_typeL_L:disturbancelow:day 0.73
## ecosystem_typeL_S:disturbancelow:day 0.65
## ecosystem_typeM:disturbancelow:day 0.72
## ecosystem_typeM_M:disturbancelow:day 0.52
## ecosystem_typeS:disturbancelow:day 0.80
## ecosystem_typeS_L:disturbancelow:day 0.01 *
## ecosystem_typeS_S:disturbancelow:day 0.99
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# --- MODEL - ANOVA --- #
car::Anova(model, type = "III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: get(response_variable_selected)
## Chisq Df Pr(>Chisq)
## (Intercept) 110.2649 1 < 2.2e-16 ***
## ecosystem_type 11.8520 7 0.1055
## disturbance 0.3227 1 0.5700
## day 0.2915 1 0.5893
## ecosystem_type:disturbance 11.0533 7 0.1363
## ecosystem_type:day 63.3876 7 3.169e-11 ***
## disturbance:day 0.0199 1 0.8879
## ecosystem_type:disturbance:day 10.9139 7 0.1424
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# --- ESTIMATE MARGINAL MEANS --- #
emmeans_output = emmeans(model,
specs = ~ ecosystem_type * disturbance * day,
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
emmeans_output## ecosystem_type disturbance day emmean SE df lower.CL upper.CL
## L high 18 6.84 0.251 115.7 6.081 7.59
## L_L high 18 6.40 0.181 77.8 5.845 6.95
## L_S high 18 5.92 0.250 116.9 5.168 6.67
## M high 18 5.58 0.248 124.6 4.835 6.32
## M_M high 18 5.94 0.183 69.6 5.381 6.50
## S high 18 1.78 0.277 128.8 0.948 2.61
## S_L high 18 3.34 0.248 125.2 2.596 4.09
## S_S high 18 2.46 0.182 75.3 1.902 3.01
## L low 18 7.25 0.252 114.4 6.492 8.01
## L_L low 18 6.38 0.182 74.8 5.826 6.94
## L_S low 18 6.38 0.250 115.3 5.621 7.13
## M low 18 5.81 0.255 108.3 5.042 6.58
## M_M low 18 6.51 0.183 69.1 5.947 7.06
## S low 18 3.87 0.250 117.8 3.119 4.62
## S_L low 18 5.42 0.249 120.2 4.674 6.17
## S_S low 18 3.86 0.187 64.8 3.292 4.44
##
## Degrees-of-freedom method: satterthwaite
## Results are given on the get (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 16 estimates# --- CODE EACH LEVEL OF DISTURBANCE AND ECOSYSTEM TYPE TO THEN PRODUCE CONTRASTS --- #
high_L = c(1, rep(0,15))
high_L_L = c(rep(0,1), 1, rep(0,14))
high_L_S = c(rep(0,2), 1, rep(0,13))
high_M = c(rep(0,3), 1, rep(0,12))
high_M_M = c(rep(0,4), 1, rep(0,11))
high_S = c(rep(0,5), 1, rep(0,10))
high_S_L = c(rep(0,6), 1, rep(0,9))
high_S_S = c(rep(0,7), 1, rep(0,8))
low_L = c(rep(0,8), 1, rep(0,7))
low_L_L = c(rep(0,9), 1, rep(0,6))
low_L_S = c(rep(0,10), 1, rep(0,5))
low_M = c(rep(0,11), 1, rep(0,4))
low_M_M = c(rep(0,12), 1, rep(0,3))
low_S = c(rep(0,13), 1, rep(0,2))
low_S_L = c(rep(0,14), 1, rep(0,1))
low_S_S = c(rep(0,15), 1)# --- PRODUCE ECOSYSTEM TYPE CONTRASTS --- #
# Set parameters
n_of_digits = 3
# Compute constrasts
contrasts = contrast(emmeans_output,
method = list("high S_L - S" = high_S_L - high_S,
"high S_L - S_S" = high_S_L - high_S_S,
"high S_S - S" = high_S_S - high_S,
"high M_M - M" = high_M_M - high_M,
"high L_S - L" = high_L_S - high_L,
"high L_S - L_L" = high_L_S - high_L_L,
"high L_L - L" = high_L_L - high_L,
"low S_L - S" = low_S_L - low_S,
"low S_L - S_S" = low_S_L - low_S_S,
"low S_S - S" = low_S_S - low_S,
"low M_M - M" = low_M_M - low_M,
"low L_S - L" = low_L_S - low_L,
"low L_S - L_L" = low_L_S - low_L_L,
"low L_L - L" = low_L_L - low_L)) %>%
as.data.frame() %>%
mutate(p.value = round(p.value, digits = n_of_digits),
estimate = round(estimate, digits = n_of_digits),
SE = round(SE, digits = n_of_digits),
df = round(df, digits = n_of_digits),
t.ratio = round(t.ratio, digits = n_of_digits),
e = "",
e = ifelse(p.value > 0.1,
"",
e),
e = ifelse(p.value < 0.05,
"*",
e),
e = ifelse(p.value < 0.01,
"**",
e),
e = ifelse(p.value < 0.001,
"***",
e)) %>%
rename(" " = e)Show results
# --- SHOW ECOSYSTEM TYPE CONSTRASTS --- #
contrasts## contrast estimate SE df t.ratio p.value
## 1 high S_L - S 1.560 0.363 155.966 4.300 0.000 ***
## 2 high S_L - S_S 0.886 0.293 154.170 3.021 0.003 **
## 3 high S_S - S 0.674 0.316 153.687 2.133 0.035 *
## 4 high M_M - M 0.359 0.293 154.093 1.225 0.222
## 5 high L_S - L -0.916 0.336 151.815 -2.723 0.007 **
## 6 high L_S - L_L -0.474 0.294 154.833 -1.614 0.109
## 7 high L_L - L -0.442 0.295 155.519 -1.498 0.136
## 8 low S_L - S 1.550 0.338 153.410 4.587 0.000 ***
## 9 low S_L - S_S 1.557 0.291 151.585 5.348 0.000 ***
## 10 low S_S - S -0.007 0.293 153.952 -0.024 0.981
## 11 low M_M - M 0.694 0.295 155.458 2.350 0.020 *
## 12 low L_S - L -0.874 0.343 156.316 -2.551 0.012 *
## 13 low L_S - L_L -0.006 0.294 155.074 -0.021 0.983
## 14 low L_L - L -0.868 0.294 155.060 -2.949 0.004 **Show model residuals residuals
# --- PLOT MODEL RESIDUALS --- #
# Simulate residuals from the fitted model
simulationOutput <- simulateResiduals(fittedModel = model,
n = 1000)
# Test for overdispersion in the model to check if variance exceeds mean
testDispersion(simulationOutput)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.004, p-value = 0.96
## alternative hypothesis: two.sided# Plot Q-Q and Residuals vs Fitted plots
plot(simulationOutput)
Evenness
response_variable_selected = "evenness_pielou"Plot original data
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp2 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Prepare data for analysis
# --- PREPARE DATA FOR ANALYSIS --- #
# Add baselines
baselines = ds_ecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(culture_ID,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_ecosystems %>%
left_join(baselines)
# Filter data and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
#!is.na(water_addition_ml),
!is.na(!!sym(response_variable_selected)),
!is.infinite(!!sym(response_variable_selected))) %>%
mutate(ecosystem_type = case_when(ecosystem_type == "Small unconnected" ~ "S",
ecosystem_type == "Medium unconnected" ~ "M",
ecosystem_type == "Large unconnected" ~ "L",
ecosystem_type == "Small connected to small" ~ "S_S",
ecosystem_type == "Small connected to large" ~ "S_L",
ecosystem_type == "Medium connected to medium" ~ "M_M",
ecosystem_type == "Large connected to large" ~ "L_L",
ecosystem_type == "Large connected to small" ~ "L_S",
TRUE ~ ecosystem_type)) %>%
ungroup()Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp2 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- MODEL - CONSTRUCT --- #
# Define formula
formula = paste("get(response_variable_selected) ~",
"ecosystem_type * disturbance * day +",
"(day | culture_ID) +",
"(day | baseline)") %>%
print()## [1] "get(response_variable_selected) ~ ecosystem_type * disturbance * day + (day | culture_ID) + (day | baseline)"# Construct model
model = try.different.optimizers.ecos(data_for_analysis,
formula)## [1] "Model successfully fitted with optimizer: bobyqa "# --- MODEL - SUMMARY --- #
print(summary(model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
## Control: lmerControl(optimizer = opt$optimizer)
##
## AIC BIC logLik deviance df.resid
## -692.0 -519.8 385.0 -770.0 573
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7 -0.5 0.1 0.6 3.9
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 6e-16 2e-08
## day 1e-18 1e-09 -1.00
## baseline (Intercept) 4e-12 2e-06
## day 2e-14 1e-07 -1.00
## Residual 2e-02 1e-01
## Number of obs: 612, groups: culture_ID, 109; baseline, 109
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8e-01 7e-02 6e+02 11.4
## ecosystem_typeL_L -6e-02 8e-02 6e+02 -0.8
## ecosystem_typeL_S -8e-02 9e-02 6e+02 -0.9
## ecosystem_typeM -2e-01 9e-02 6e+02 -2.2
## ecosystem_typeM_M -8e-02 8e-02 6e+02 -0.9
## ecosystem_typeS 5e-01 1e-01 6e+02 4.3
## ecosystem_typeS_L 6e-02 1e-01 6e+02 0.6
## ecosystem_typeS_S 5e-01 8e-02 6e+02 5.5
## disturbancelow 2e-01 9e-02 6e+02 1.6
## day 5e-04 3e-03 6e+02 0.2
## ecosystem_typeL_L:disturbancelow -9e-02 1e-01 6e+02 -0.8
## ecosystem_typeL_S:disturbancelow -1e-01 1e-01 6e+02 -0.9
## ecosystem_typeM:disturbancelow -8e-02 1e-01 6e+02 -0.6
## ecosystem_typeM_M:disturbancelow -2e-01 1e-01 6e+02 -1.5
## ecosystem_typeS:disturbancelow -5e-01 1e-01 6e+02 -3.5
## ecosystem_typeS_L:disturbancelow -3e-01 1e-01 6e+02 -2.2
## ecosystem_typeS_S:disturbancelow -6e-01 1e-01 6e+02 -5.3
## ecosystem_typeL_L:day 5e-03 4e-03 6e+02 1.1
## ecosystem_typeL_S:day 5e-03 5e-03 6e+02 1.0
## ecosystem_typeM:day 1e-02 5e-03 6e+02 2.3
## ecosystem_typeM_M:day 5e-03 4e-03 6e+02 1.1
## ecosystem_typeS:day -5e-02 6e-03 6e+02 -8.0
## ecosystem_typeS_L:day 4e-03 5e-03 6e+02 0.7
## ecosystem_typeS_S:day -3e-02 4e-03 6e+02 -6.8
## disturbancelow:day -8e-03 5e-03 6e+02 -1.6
## ecosystem_typeL_L:disturbancelow:day 4e-03 6e-03 6e+02 0.7
## ecosystem_typeL_S:disturbancelow:day 6e-03 7e-03 6e+02 0.9
## ecosystem_typeM:disturbancelow:day 6e-03 7e-03 6e+02 0.9
## ecosystem_typeM_M:disturbancelow:day 6e-03 6e-03 6e+02 1.0
## ecosystem_typeS:disturbancelow:day 5e-02 8e-03 6e+02 6.3
## ecosystem_typeS_L:disturbancelow:day 1e-02 7e-03 6e+02 1.6
## ecosystem_typeS_S:disturbancelow:day 4e-02 6e-03 6e+02 6.7
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ecosystem_typeL_L 0.44
## ecosystem_typeL_S 0.37
## ecosystem_typeM 0.03 *
## ecosystem_typeM_M 0.35
## ecosystem_typeS 2e-05 ***
## ecosystem_typeS_L 0.52
## ecosystem_typeS_S 7e-08 ***
## disturbancelow 0.10
## day 0.88
## ecosystem_typeL_L:disturbancelow 0.42
## ecosystem_typeL_S:disturbancelow 0.38
## ecosystem_typeM:disturbancelow 0.53
## ecosystem_typeM_M:disturbancelow 0.13
## ecosystem_typeS:disturbancelow 6e-04 ***
## ecosystem_typeS_L:disturbancelow 0.03 *
## ecosystem_typeS_S:disturbancelow 1e-07 ***
## ecosystem_typeL_L:day 0.26
## ecosystem_typeL_S:day 0.30
## ecosystem_typeM:day 0.02 *
## ecosystem_typeM_M:day 0.25
## ecosystem_typeS:day 7e-15 ***
## ecosystem_typeS_L:day 0.47
## ecosystem_typeS_S:day 3e-11 ***
## disturbancelow:day 0.11
## ecosystem_typeL_L:disturbancelow:day 0.50
## ecosystem_typeL_S:disturbancelow:day 0.37
## ecosystem_typeM:disturbancelow:day 0.37
## ecosystem_typeM_M:disturbancelow:day 0.30
## ecosystem_typeS:disturbancelow:day 6e-10 ***
## ecosystem_typeS_L:disturbancelow:day 0.10
## ecosystem_typeS_S:disturbancelow:day 6e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# --- MODEL - ANOVA --- #
car::Anova(model, type = "III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: get(response_variable_selected)
## Chisq Df Pr(>Chisq)
## (Intercept) 130.1357 1 < 2.2e-16 ***
## ecosystem_type 121.9831 7 < 2.2e-16 ***
## disturbance 2.6800 1 0.1016
## day 0.0240 1 0.8769
## ecosystem_type:disturbance 53.2690 7 3.28e-09 ***
## ecosystem_type:day 225.4252 7 < 2.2e-16 ***
## disturbance:day 2.5267 1 0.1119
## ecosystem_type:disturbance:day 111.4767 7 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# --- ESTIMATE MARGINAL MEANS --- #
emmeans_output = emmeans(model,
specs = ~ ecosystem_type * disturbance * day,
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
emmeans_output## ecosystem_type disturbance day emmean SE df lower.CL upper.CL
## L high 17.7 0.767 0.0236 612 0.697 0.836
## L_L high 17.7 0.789 0.0167 612 0.739 0.838
## L_S high 17.7 0.772 0.0236 612 0.702 0.842
## M high 17.7 0.756 0.0236 612 0.687 0.826
## M_M high 17.7 0.777 0.0167 612 0.728 0.826
## S high 17.7 0.415 0.0349 612 0.312 0.518
## S_L high 17.7 0.899 0.0276 612 0.817 0.981
## S_S high 17.7 0.687 0.0199 612 0.629 0.746
## L low 17.7 0.783 0.0236 612 0.713 0.853
## L_L low 17.7 0.783 0.0167 612 0.734 0.832
## L_S low 17.7 0.782 0.0236 612 0.712 0.852
## M low 17.7 0.799 0.0236 612 0.730 0.869
## M_M low 17.7 0.729 0.0167 612 0.680 0.779
## S low 17.7 0.783 0.0249 612 0.709 0.856
## S_L low 17.7 0.823 0.0236 612 0.753 0.893
## S_S low 17.7 0.814 0.0176 612 0.762 0.866
##
## Degrees-of-freedom method: satterthwaite
## Results are given on the get (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 16 estimates# --- CODE EACH LEVEL OF DISTURBANCE AND ECOSYSTEM TYPE TO THEN PRODUCE CONTRASTS --- #
high_L = c(1, rep(0,15))
high_L_L = c(rep(0,1), 1, rep(0,14))
high_L_S = c(rep(0,2), 1, rep(0,13))
high_M = c(rep(0,3), 1, rep(0,12))
high_M_M = c(rep(0,4), 1, rep(0,11))
high_S = c(rep(0,5), 1, rep(0,10))
high_S_L = c(rep(0,6), 1, rep(0,9))
high_S_S = c(rep(0,7), 1, rep(0,8))
low_L = c(rep(0,8), 1, rep(0,7))
low_L_L = c(rep(0,9), 1, rep(0,6))
low_L_S = c(rep(0,10), 1, rep(0,5))
low_M = c(rep(0,11), 1, rep(0,4))
low_M_M = c(rep(0,12), 1, rep(0,3))
low_S = c(rep(0,13), 1, rep(0,2))
low_S_L = c(rep(0,14), 1, rep(0,1))
low_S_S = c(rep(0,15), 1)# --- PRODUCE ECOSYSTEM TYPE CONTRASTS --- #
# Set parameters
n_of_digits = 3
# Compute constrasts
contrasts = contrast(emmeans_output,
method = list("high S_L - S" = high_S_L - high_S,
"high S_L - S_S" = high_S_L - high_S_S,
"high S_S - S" = high_S_S - high_S,
"high M_M - M" = high_M_M - high_M,
"high L_S - L" = high_L_S - high_L,
"high L_S - L_L" = high_L_S - high_L_L,
"high L_L - L" = high_L_L - high_L,
"low S_L - S" = low_S_L - low_S,
"low S_L - S_S" = low_S_L - low_S_S,
"low S_S - S" = low_S_S - low_S,
"low M_M - M" = low_M_M - low_M,
"low L_S - L" = low_L_S - low_L,
"low L_S - L_L" = low_L_S - low_L_L,
"low L_L - L" = low_L_L - low_L)) %>%
as.data.frame() %>%
mutate(p.value = round(p.value, digits = n_of_digits),
estimate = round(estimate, digits = n_of_digits),
SE = round(SE, digits = n_of_digits),
df = round(df, digits = n_of_digits),
t.ratio = round(t.ratio, digits = n_of_digits),
e = "",
e = ifelse(p.value > 0.1,
"",
e),
e = ifelse(p.value < 0.05,
"*",
e),
e = ifelse(p.value < 0.01,
"**",
e),
e = ifelse(p.value < 0.001,
"***",
e)) %>%
rename(" " = e)Show results
# --- SHOW ECOSYSTEM TYPE CONSTRASTS --- #
contrasts## contrast estimate SE df t.ratio p.value
## 1 high S_L - S 0.484 0.044 612 10.869 0.000 ***
## 2 high S_L - S_S 0.211 0.034 612 6.207 0.000 ***
## 3 high S_S - S 0.272 0.040 612 6.786 0.000 ***
## 4 high M_M - M 0.021 0.029 612 0.715 0.475
## 5 high L_S - L 0.006 0.033 612 0.166 0.868
## 6 high L_S - L_L -0.017 0.029 612 -0.575 0.566
## 7 high L_L - L 0.022 0.029 612 0.766 0.444
## 8 low S_L - S 0.040 0.034 612 1.173 0.241
## 9 low S_L - S_S 0.009 0.029 612 0.299 0.765
## 10 low S_S - S 0.031 0.030 612 1.032 0.303
## 11 low M_M - M -0.070 0.029 612 -2.423 0.016 *
## 12 low L_S - L -0.001 0.033 612 -0.027 0.978
## 13 low L_S - L_L -0.001 0.029 612 -0.023 0.982
## 14 low L_L - L 0.000 0.029 612 -0.009 0.993Show model residuals residuals
# --- PLOT MODEL RESIDUALS --- #
# Simulate residuals from the fitted model
simulationOutput <- simulateResiduals(fittedModel = model,
n = 1000)
# Test for overdispersion in the model to check if variance exceeds mean
testDispersion(simulationOutput)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.0036, p-value = 0.944
## alternative hypothesis: two.sided# Plot Q-Q and Residuals vs Fitted plots
plot(simulationOutput)
Median Size
response_variable_selected = "median_body_area_µm2"Plot original data
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp2 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Prepare data for analysis
# --- PREPARE DATA FOR ANALYSIS --- #
# Add baselines
baselines = ds_ecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(culture_ID,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_ecosystems %>%
left_join(baselines)
# Filter data and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
#!is.na(water_addition_ml),
!is.na(!!sym(response_variable_selected)),
!is.infinite(!!sym(response_variable_selected))) %>%
mutate(ecosystem_type = case_when(ecosystem_type == "Small unconnected" ~ "S",
ecosystem_type == "Medium unconnected" ~ "M",
ecosystem_type == "Large unconnected" ~ "L",
ecosystem_type == "Small connected to small" ~ "S_S",
ecosystem_type == "Small connected to large" ~ "S_L",
ecosystem_type == "Medium connected to medium" ~ "M_M",
ecosystem_type == "Large connected to large" ~ "L_L",
ecosystem_type == "Large connected to small" ~ "L_S",
TRUE ~ ecosystem_type)) %>%
ungroup()Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp2 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- MODEL - CONSTRUCT --- #
# Define formula
formula = paste("get(response_variable_selected) ~",
"ecosystem_type * disturbance * day +",
"(day | culture_ID) +",
"(day | baseline)") %>%
print()## [1] "get(response_variable_selected) ~ ecosystem_type * disturbance * day + (day | culture_ID) + (day | baseline)"# Construct model
model = try.different.optimizers.ecos(data_for_analysis,
formula)## [1] "Model successfully fitted with optimizer: bobyqa "# --- MODEL - SUMMARY --- #
print(summary(model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
## Control: lmerControl(optimizer = opt$optimizer)
##
## AIC BIC logLik deviance df.resid
## 10359.9 10534.0 -5141.0 10281.9 602
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3 -0.5 0.0 0.5 3.8
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 7e+03 83
## day 8e+01 9 -1.00
## baseline (Intercept) 4e+04 204
## day 5e+02 21 -1.00
## Residual 5e+05 702
## Number of obs: 641, groups: culture_ID, 109; baseline, 109
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3e+03 4e+02 3e+02 8.9
## ecosystem_typeL_L -4e+02 5e+02 3e+02 -1.0
## ecosystem_typeL_S 3e+02 5e+02 3e+02 0.5
## ecosystem_typeM -2e+01 5e+02 3e+02 0.0
## ecosystem_typeM_M 7e+01 5e+02 3e+02 0.1
## ecosystem_typeS -1e+03 6e+02 3e+02 -2.1
## ecosystem_typeS_L -1e+03 5e+02 3e+02 -1.8
## ecosystem_typeS_S -1e+01 5e+02 3e+02 0.0
## disturbancelow -1e+02 5e+02 3e+02 -0.2
## day -7e+00 2e+01 2e+02 -0.3
## ecosystem_typeL_L:disturbancelow -4e+02 6e+02 3e+02 -0.6
## ecosystem_typeL_S:disturbancelow -1e+03 7e+02 3e+02 -1.5
## ecosystem_typeM:disturbancelow 6e+01 7e+02 3e+02 0.1
## ecosystem_typeM_M:disturbancelow 6e+01 6e+02 3e+02 0.1
## ecosystem_typeS:disturbancelow 1e+03 8e+02 3e+02 1.5
## ecosystem_typeS_L:disturbancelow 9e+02 7e+02 3e+02 1.2
## ecosystem_typeS_S:disturbancelow -2e+02 7e+02 3e+02 -0.3
## ecosystem_typeL_L:day 6e+01 3e+01 2e+02 2.1
## ecosystem_typeL_S:day -3e-01 3e+01 2e+02 0.0
## ecosystem_typeM:day 3e+01 3e+01 2e+02 1.0
## ecosystem_typeM_M:day 2e+01 3e+01 2e+02 0.8
## ecosystem_typeS:day 7e+01 4e+01 2e+02 1.9
## ecosystem_typeS_L:day 9e+01 3e+01 2e+02 3.0
## ecosystem_typeS_S:day -4e+00 3e+01 2e+02 -0.1
## disturbancelow:day 2e+01 3e+01 2e+02 0.8
## ecosystem_typeL_L:disturbancelow:day -1e+00 4e+01 2e+02 0.0
## ecosystem_typeL_S:disturbancelow:day 5e+01 4e+01 2e+02 1.1
## ecosystem_typeM:disturbancelow:day -2e+01 4e+01 2e+02 -0.6
## ecosystem_typeM_M:disturbancelow:day -3e+01 4e+01 2e+02 -0.7
## ecosystem_typeS:disturbancelow:day -6e+01 5e+01 2e+02 -1.3
## ecosystem_typeS_L:disturbancelow:day -7e+01 4e+01 2e+02 -1.7
## ecosystem_typeS_S:disturbancelow:day 2e+01 4e+01 2e+02 0.6
## Pr(>|t|)
## (Intercept) <2e-16 ***
## ecosystem_typeL_L 0.333
## ecosystem_typeL_S 0.584
## ecosystem_typeM 0.968
## ecosystem_typeM_M 0.882
## ecosystem_typeS 0.036 *
## ecosystem_typeS_L 0.067 .
## ecosystem_typeS_S 0.979
## disturbancelow 0.850
## day 0.734
## ecosystem_typeL_L:disturbancelow 0.535
## ecosystem_typeL_S:disturbancelow 0.146
## ecosystem_typeM:disturbancelow 0.933
## ecosystem_typeM_M:disturbancelow 0.932
## ecosystem_typeS:disturbancelow 0.146
## ecosystem_typeS_L:disturbancelow 0.218
## ecosystem_typeS_S:disturbancelow 0.802
## ecosystem_typeL_L:day 0.037 *
## ecosystem_typeL_S:day 0.993
## ecosystem_typeM:day 0.303
## ecosystem_typeM_M:day 0.423
## ecosystem_typeS:day 0.059 .
## ecosystem_typeS_L:day 0.003 **
## ecosystem_typeS_S:day 0.895
## disturbancelow:day 0.417
## ecosystem_typeL_L:disturbancelow:day 0.978
## ecosystem_typeL_S:disturbancelow:day 0.257
## ecosystem_typeM:disturbancelow:day 0.562
## ecosystem_typeM_M:disturbancelow:day 0.485
## ecosystem_typeS:disturbancelow:day 0.192
## ecosystem_typeS_L:disturbancelow:day 0.099 .
## ecosystem_typeS_S:disturbancelow:day 0.551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# --- MODEL - ANOVA --- #
car::Anova(model, type = "III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: get(response_variable_selected)
## Chisq Df Pr(>Chisq)
## (Intercept) 79.3960 1 < 2.2e-16 ***
## ecosystem_type 13.9140 7 0.052733 .
## disturbance 0.0358 1 0.849879
## day 0.1156 1 0.733849
## ecosystem_type:disturbance 12.4010 7 0.088118 .
## ecosystem_type:day 20.7691 7 0.004127 **
## disturbance:day 0.6627 1 0.415605
## ecosystem_type:disturbance:day 13.0090 7 0.071889 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# --- ESTIMATE MARGINAL MEANS --- #
emmeans_output = emmeans(model,
specs = ~ ecosystem_type * disturbance * day,
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
emmeans_output## ecosystem_type disturbance day emmean SE df lower.CL upper.CL
## L high 17.8 3207 155 126 2741 3672
## L_L high 17.8 3747 109 126 3418 4076
## L_S high 17.8 3492 155 126 3027 3958
## M high 17.8 3745 155 126 3280 4211
## M_M high 17.8 3651 109 126 3322 3980
## S high 17.8 3165 198 202 2574 3756
## S_L high 17.8 3853 155 126 3387 4318
## S_S high 17.8 3130 115 146 2784 3476
## L low 17.8 3547 155 126 3082 4012
## L_L low 17.8 3667 109 126 3338 3996
## L_S low 17.8 3614 155 126 3148 4079
## M low 17.8 3704 155 126 3238 4169
## M_M low 17.8 3583 109 126 3254 3912
## S low 17.8 3569 157 131 3098 4041
## S_L low 17.8 3849 155 126 3383 4314
## S_S low 17.8 3710 109 126 3380 4039
##
## Degrees-of-freedom method: satterthwaite
## Results are given on the get (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 16 estimates# --- CODE EACH LEVEL OF DISTURBANCE AND ECOSYSTEM TYPE TO THEN PRODUCE CONTRASTS --- #
high_L = c(1, rep(0,15))
high_L_L = c(rep(0,1), 1, rep(0,14))
high_L_S = c(rep(0,2), 1, rep(0,13))
high_M = c(rep(0,3), 1, rep(0,12))
high_M_M = c(rep(0,4), 1, rep(0,11))
high_S = c(rep(0,5), 1, rep(0,10))
high_S_L = c(rep(0,6), 1, rep(0,9))
high_S_S = c(rep(0,7), 1, rep(0,8))
low_L = c(rep(0,8), 1, rep(0,7))
low_L_L = c(rep(0,9), 1, rep(0,6))
low_L_S = c(rep(0,10), 1, rep(0,5))
low_M = c(rep(0,11), 1, rep(0,4))
low_M_M = c(rep(0,12), 1, rep(0,3))
low_S = c(rep(0,13), 1, rep(0,2))
low_S_L = c(rep(0,14), 1, rep(0,1))
low_S_S = c(rep(0,15), 1)# --- PRODUCE ECOSYSTEM TYPE CONTRASTS --- #
# Set parameters
n_of_digits = 3
# Compute constrasts
contrasts = contrast(emmeans_output,
method = list("high S_L - S" = high_S_L - high_S,
"high S_L - S_S" = high_S_L - high_S_S,
"high S_S - S" = high_S_S - high_S,
"high M_M - M" = high_M_M - high_M,
"high L_S - L" = high_L_S - high_L,
"high L_S - L_L" = high_L_S - high_L_L,
"high L_L - L" = high_L_L - high_L,
"low S_L - S" = low_S_L - low_S,
"low S_L - S_S" = low_S_L - low_S_S,
"low S_S - S" = low_S_S - low_S,
"low M_M - M" = low_M_M - low_M,
"low L_S - L" = low_L_S - low_L,
"low L_S - L_L" = low_L_S - low_L_L,
"low L_L - L" = low_L_L - low_L)) %>%
as.data.frame() %>%
mutate(p.value = round(p.value, digits = n_of_digits),
estimate = round(estimate, digits = n_of_digits),
SE = round(SE, digits = n_of_digits),
df = round(df, digits = n_of_digits),
t.ratio = round(t.ratio, digits = n_of_digits),
e = "",
e = ifelse(p.value > 0.1,
"",
e),
e = ifelse(p.value < 0.05,
"*",
e),
e = ifelse(p.value < 0.01,
"**",
e),
e = ifelse(p.value < 0.001,
"***",
e)) %>%
rename(" " = e)Show results
# --- SHOW ECOSYSTEM TYPE CONSTRASTS --- #
contrasts## contrast estimate SE df t.ratio p.value
## 1 high S_L - S 687.553 251.380 167.153 2.735 0.007 **
## 2 high S_L - S_S 723.004 193.148 132.515 3.743 0.000 ***
## 3 high S_S - S -35.451 229.229 185.647 -0.155 0.877
## 4 high M_M - M -93.928 189.642 125.539 -0.495 0.621
## 5 high L_S - L 285.749 218.980 125.539 1.305 0.194
## 6 high L_S - L_L -254.951 189.642 125.539 -1.344 0.181
## 7 high L_L - L 540.699 189.642 125.539 2.851 0.005 **
## 8 low S_L - S 279.243 220.580 128.428 1.266 0.208
## 9 low S_L - S_S 139.105 189.642 125.539 0.734 0.465
## 10 low S_S - S 140.138 191.487 129.392 0.732 0.466
## 11 low M_M - M -120.492 189.642 125.539 -0.635 0.526
## 12 low L_S - L 66.693 218.980 125.539 0.305 0.761
## 13 low L_S - L_L -53.114 189.642 125.539 -0.280 0.780
## 14 low L_L - L 119.807 189.642 125.539 0.632 0.529Show model residuals residuals
# --- PLOT MODEL RESIDUALS --- #
# Simulate residuals from the fitted model
simulationOutput <- simulateResiduals(fittedModel = model,
n = 1000)
# Test for overdispersion in the model to check if variance exceeds mean
testDispersion(simulationOutput)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.0018, p-value = 0.96
## alternative hypothesis: two.sided# Plot Q-Q and Residuals vs Fitted plots
plot(simulationOutput)
Auto/Hetero
response_variable_selected = "auto_hetero_ratio"Plot original data
# --- ORIGINAL DATA - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp2 = plot.all.patches.points(data = ds_ecosystems %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_summary()`).
Prepare data for analysis
# --- PREPARE DATA FOR ANALYSIS --- #
# Add baselines
baselines = ds_ecosystems %>%
filter(time_point == time_point_of_baselines) %>%
select(culture_ID,
all_of(response_variable_selected)) %>%
rename(baseline = all_of(response_variable_selected))
data_for_analysis = ds_ecosystems %>%
left_join(baselines)
# Filter data and change level names
data_for_analysis = data_for_analysis %>%
filter(time_point %in% time_points_model,
#!is.na(water_addition_ml),
!is.na(!!sym(response_variable_selected)),
!is.infinite(!!sym(response_variable_selected))) %>%
mutate(ecosystem_type = case_when(ecosystem_type == "Small unconnected" ~ "S",
ecosystem_type == "Medium unconnected" ~ "M",
ecosystem_type == "Large unconnected" ~ "L",
ecosystem_type == "Small connected to small" ~ "S_S",
ecosystem_type == "Small connected to large" ~ "S_L",
ecosystem_type == "Medium connected to medium" ~ "M_M",
ecosystem_type == "Large connected to large" ~ "L_L",
ecosystem_type == "Large connected to small" ~ "L_S",
TRUE ~ ecosystem_type)) %>%
ungroup()Plot data for analysis means
# --- DATA FOR ANALYSIS - PLOT MEAN ± 95% CI --- #
p1 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "high"),
response_variable_selected) +
annotate("text",
x = Inf,
y = Inf,
label = "High disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") + # Adjust position and style
theme(axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank()) # Remove x-axis text## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp2 = plot.all.patches.points(data = data_for_analysis %>%
filter(disturbance == "low"),
response_variable_selected) +
theme(legend.position = "none") +
annotate("text",
x = Inf,
y = Inf,
label = "Low disturbance",
hjust = 1.1,
vjust = 1.1,
size = 5,
fontface = "bold") # Adjust position and style
# Arrange the plots vertically
ggarrange(p1,
p2,
ncol = 1,
nrow = 2,
heights = c(1, 0.7))
Compute model results
# --- MODEL - CONSTRUCT --- #
# Define formula
formula = paste("get(response_variable_selected) ~",
"ecosystem_type * disturbance * day +",
"(day | culture_ID) +",
"(day | baseline)") %>%
print()## [1] "get(response_variable_selected) ~ ecosystem_type * disturbance * day + (day | culture_ID) + (day | baseline)"# Construct model
model = try.different.optimizers.ecos(data_for_analysis,
formula)## [1] "Model successfully fitted with optimizer: bobyqa "# --- MODEL - SUMMARY --- #
print(summary(model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
## Control: lmerControl(optimizer = opt$optimizer)
##
## AIC BIC logLik deviance df.resid
## -447.3 -273.4 262.7 -525.3 600
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5 -0.5 -0.1 0.3 8.0
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-09 3e-05
## day 6e-12 2e-06 -1.00
## baseline (Intercept) 1e-02 1e-01
## day 9e-05 9e-03 -1.00
## Residual 2e-02 1e-01
## Number of obs: 639, groups: culture_ID, 109; baseline, 104
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -2e-01 9e-02 2e+02 -1.7
## ecosystem_typeL_L 2e-01 1e-01 2e+02 1.9
## ecosystem_typeL_S 1e-01 1e-01 2e+02 0.9
## ecosystem_typeM 2e-01 1e-01 2e+02 1.7
## ecosystem_typeM_M 1e-01 1e-01 2e+02 1.2
## ecosystem_typeS 3e-01 1e-01 2e+02 2.2
## ecosystem_typeS_L 4e-01 1e-01 2e+02 2.9
## ecosystem_typeS_S 3e-01 1e-01 2e+02 3.0
## disturbancelow 4e-01 1e-01 2e+02 3.1
## day 2e-02 6e-03 1e+02 4.1
## ecosystem_typeL_L:disturbancelow -4e-01 2e-01 2e+02 -2.6
## ecosystem_typeL_S:disturbancelow -3e-01 2e-01 2e+02 -1.8
## ecosystem_typeM:disturbancelow -4e-01 2e-01 2e+02 -2.4
## ecosystem_typeM_M:disturbancelow -4e-01 2e-01 2e+02 -2.4
## ecosystem_typeS:disturbancelow -5e-01 2e-01 2e+02 -2.7
## ecosystem_typeS_L:disturbancelow -6e-01 2e-01 2e+02 -3.1
## ecosystem_typeS_S:disturbancelow -6e-01 2e-01 2e+02 -3.7
## ecosystem_typeL_L:day -2e-02 7e-03 1e+02 -2.7
## ecosystem_typeL_S:day -8e-03 8e-03 1e+02 -1.0
## ecosystem_typeM:day -2e-02 8e-03 1e+02 -2.7
## ecosystem_typeM_M:day -1e-02 7e-03 1e+02 -1.9
## ecosystem_typeS:day -3e-02 9e-03 1e+02 -3.3
## ecosystem_typeS_L:day -3e-02 8e-03 2e+02 -3.9
## ecosystem_typeS_S:day -3e-02 7e-03 1e+02 -4.5
## disturbancelow:day -3e-02 8e-03 1e+02 -3.5
## ecosystem_typeL_L:disturbancelow:day 3e-02 1e-02 1e+02 3.1
## ecosystem_typeL_S:disturbancelow:day 2e-02 1e-02 1e+02 1.7
## ecosystem_typeM:disturbancelow:day 4e-02 1e-02 1e+02 3.1
## ecosystem_typeM_M:disturbancelow:day 3e-02 1e-02 1e+02 2.7
## ecosystem_typeS:disturbancelow:day 4e-02 1e-02 1e+02 3.2
## ecosystem_typeS_L:disturbancelow:day 4e-02 1e-02 2e+02 3.3
## ecosystem_typeS_S:disturbancelow:day 4e-02 1e-02 1e+02 4.2
## Pr(>|t|)
## (Intercept) 0.098 .
## ecosystem_typeL_L 0.056 .
## ecosystem_typeL_S 0.375
## ecosystem_typeM 0.095 .
## ecosystem_typeM_M 0.228
## ecosystem_typeS 0.030 *
## ecosystem_typeS_L 0.004 **
## ecosystem_typeS_S 0.003 **
## disturbancelow 0.002 **
## day 7e-05 ***
## ecosystem_typeL_L:disturbancelow 0.009 **
## ecosystem_typeL_S:disturbancelow 0.070 .
## ecosystem_typeM:disturbancelow 0.016 *
## ecosystem_typeM_M:disturbancelow 0.016 *
## ecosystem_typeS:disturbancelow 0.008 **
## ecosystem_typeS_L:disturbancelow 0.002 **
## ecosystem_typeS_S:disturbancelow 2e-04 ***
## ecosystem_typeL_L:day 0.007 **
## ecosystem_typeL_S:day 0.298
## ecosystem_typeM:day 0.008 **
## ecosystem_typeM_M:day 0.066 .
## ecosystem_typeS:day 0.001 **
## ecosystem_typeS_L:day 1e-04 ***
## ecosystem_typeS_S:day 2e-05 ***
## disturbancelow:day 6e-04 ***
## ecosystem_typeL_L:disturbancelow:day 0.002 **
## ecosystem_typeL_S:disturbancelow:day 0.093 .
## ecosystem_typeM:disturbancelow:day 0.003 **
## ecosystem_typeM_M:disturbancelow:day 0.008 **
## ecosystem_typeS:disturbancelow:day 0.002 **
## ecosystem_typeS_L:disturbancelow:day 0.001 **
## ecosystem_typeS_S:disturbancelow:day 4e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# --- MODEL - ANOVA --- #
car::Anova(model, type = "III")## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: get(response_variable_selected)
## Chisq Df Pr(>Chisq)
## (Intercept) 2.7670 1 0.096224 .
## ecosystem_type 15.6134 7 0.028893 *
## disturbance 9.6489 1 0.001895 **
## day 17.0660 1 3.610e-05 ***
## ecosystem_type:disturbance 16.4222 7 0.021527 *
## ecosystem_type:day 32.9062 7 2.756e-05 ***
## disturbance:day 12.3798 1 0.000434 ***
## ecosystem_type:disturbance:day 22.2662 7 0.002285 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# --- ESTIMATE MARGINAL MEANS --- #
emmeans_output = emmeans(model,
specs = ~ ecosystem_type * disturbance * day,
adjust = "sidak",
bias.adj = TRUE,
lmer.df = "satterthwaite")
emmeans_output## ecosystem_type disturbance day emmean SE df lower.CL upper.CL
## L high 17.8 0.2681 0.0349 135 0.16348 0.373
## L_L high 17.8 0.1483 0.0238 182 0.07714 0.219
## L_S high 17.8 0.2331 0.0349 135 0.12846 0.338
## M high 17.8 0.0943 0.0349 135 -0.01038 0.199
## M_M high 17.8 0.1727 0.0247 135 0.09868 0.247
## S high 17.8 0.0432 0.0439 202 -0.08773 0.174
## S_L high 17.8 0.1013 0.0330 251 0.00313 0.199
## S_S high 17.8 0.0389 0.0255 196 -0.03734 0.115
## L low 17.8 0.1648 0.0349 135 0.06019 0.269
## L_L low 17.8 0.1655 0.0238 182 0.09432 0.237
## L_S low 17.8 0.1403 0.0349 135 0.03565 0.245
## M low 17.8 0.1682 0.0349 135 0.06355 0.273
## M_M low 17.8 0.1581 0.0238 182 0.08700 0.229
## S low 17.8 0.1202 0.0354 141 0.01408 0.226
## S_L low 17.8 0.0773 0.0327 236 -0.01993 0.175
## S_S low 17.8 0.0918 0.0247 135 0.01782 0.166
##
## Degrees-of-freedom method: satterthwaite
## Results are given on the get (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 16 estimates# --- CODE EACH LEVEL OF DISTURBANCE AND ECOSYSTEM TYPE TO THEN PRODUCE CONTRASTS --- #
high_L = c(1, rep(0,15))
high_L_L = c(rep(0,1), 1, rep(0,14))
high_L_S = c(rep(0,2), 1, rep(0,13))
high_M = c(rep(0,3), 1, rep(0,12))
high_M_M = c(rep(0,4), 1, rep(0,11))
high_S = c(rep(0,5), 1, rep(0,10))
high_S_L = c(rep(0,6), 1, rep(0,9))
high_S_S = c(rep(0,7), 1, rep(0,8))
low_L = c(rep(0,8), 1, rep(0,7))
low_L_L = c(rep(0,9), 1, rep(0,6))
low_L_S = c(rep(0,10), 1, rep(0,5))
low_M = c(rep(0,11), 1, rep(0,4))
low_M_M = c(rep(0,12), 1, rep(0,3))
low_S = c(rep(0,13), 1, rep(0,2))
low_S_L = c(rep(0,14), 1, rep(0,1))
low_S_S = c(rep(0,15), 1)# --- PRODUCE ECOSYSTEM TYPE CONTRASTS --- #
# Set parameters
n_of_digits = 3
# Compute constrasts
contrasts = contrast(emmeans_output,
method = list("high L - S" = high_L - high_S,
"high M - S" = high_M - high_S,
"low L - S" = low_L - low_S,
"low M - S" = low_M - low_S)) %>%
as.data.frame() %>%
mutate(p.value = round(p.value, digits = n_of_digits),
estimate = round(estimate, digits = n_of_digits),
SE = round(SE, digits = n_of_digits),
df = round(df, digits = n_of_digits),
t.ratio = round(t.ratio, digits = n_of_digits),
e = "",
e = ifelse(p.value > 0.1,
"",
e),
e = ifelse(p.value < 0.05,
"*",
e),
e = ifelse(p.value < 0.01,
"**",
e),
e = ifelse(p.value < 0.001,
"***",
e)) %>%
rename(" " = e)Show results
# --- SHOW ECOSYSTEM TYPE CONSTRASTS --- #
contrasts## contrast estimate SE df t.ratio p.value
## 1 high L - S 0.225 0.056 172.002 4.013 0.000 ***
## 2 high M - S 0.051 0.056 172.002 0.911 0.364
## 3 low L - S 0.045 0.050 137.957 0.899 0.370
## 4 low M - S 0.048 0.050 137.957 0.967 0.335Show model residuals residuals
# --- PLOT MODEL RESIDUALS --- #
# Simulate residuals from the fitted model
simulationOutput <- simulateResiduals(fittedModel = model,
n = 1000)
# Test for overdispersion in the model to check if variance exceeds mean
testDispersion(simulationOutput)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 0.99756, p-value = 0.998
## alternative hypothesis: two.sided# Plot Q-Q and Residuals vs Fitted plots
plot(simulationOutput)
BEF
# --- PLOT BIOMASS-SPECIES RICHNESS RELATIONSHIP --- #
ds_ecosystems %>%
filter(is.na(species_richness) != TRUE) %>%
ggplot(aes(x = species_richness,
y = bioarea_mm2_per_ml)) +
geom_point() +
xlim(0, length(protist_species)) +
labs(x = axis_names$axis_name[axis_names$variable == "species_richness"],
y = axis_names$axis_name[axis_names$variable == "bioarea_mm2_per_ml"]) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm"))
# --- PLOT BIOMASS-SHANNON RELATIONSHIP --- #
ds_ecosystems %>%
filter(is.na(shannon) != TRUE) %>%
ggplot(aes(x = shannon,
y = bioarea_mm2_per_ml)) +
geom_point() +
labs(x = axis_names$axis_name[axis_names$variable == "shannon"],
y = axis_names$axis_name[axis_names$variable == "bioarea_mm2_per_ml"]) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm"))
# --- PLOT BIOMASS-EVENESS RELATIONSHIP --- #
ds_ecosystems %>%
filter(is.na(evenness_pielou) != TRUE) %>%
ggplot(aes(x = evenness_pielou,
y = bioarea_mm2_per_ml)) +
geom_point() +
labs(x = axis_names$axis_name[axis_names$variable == "evenness_pielou"],
y = axis_names$axis_name[axis_names$variable == "bioarea_mm2_per_ml"]) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm"))
Populations
Figures
High disturbance
disturbance_selected = "high"# --- PREPARE DATA FOR PLOTTING --- #
# Define what you want to plot
metaecosystem_type_selected = c("Medium-Medium",
"Small-Large")
# Filter dataset
data_for_plotting = ds_metaecosystems %>%
filter(metaecosystem_type %in% metaecosystem_type_selected,
disturbance == disturbance_selected)# Write function to plot a response variable. Afterwards you can use this function to plot alpha, beta, gamma diversity, and biomass.
plot.single.plot = function(response_variable_selected){
data_for_plotting %>%
filter(metaecosystem_type %in% metaecosystem_type_selected,
!is.na(!!sym(response_variable_selected))) %>%
summarySE(measurevar = response_variable_selected,
groupvars = c("day", "ecosystem_size_symmetry", "connection")) %>%
ggplot(aes(x = day,
y = get(response_variable_selected),
group = interaction(day, ecosystem_size_symmetry, connection),
color = ecosystem_size_symmetry,
linetype = connection)) +
geom_point(stat = "summary",
fun = "mean",
position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(stat = "summary",
fun = "mean",
aes(group = interaction(ecosystem_size_symmetry, connection)),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
geom_errorbar(aes(ymax = get(response_variable_selected) + ci,
ymin = get(response_variable_selected) - ci),
width = width_errorbar,
position = position_dodge(dodging)) +
labs(x = axis_names$axis_name[axis_names$variable == "day"],
y = axis_names$axis_name[axis_names$variable == response_variable_selected],
color = "") +
scale_color_manual(values = treatment_colours_paper) +
scale_linetype_manual(values = treatment_linetype_paper) +
geom_vline(xintercept = resource_flow_days,
linetype = resource_flow_line_type,
color = resource_flow_line_colour,
linewidth = resource_flow_line_width) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm")) +
guides(color = guide_legend(title = NULL,
nrow = 2),
linetype = guide_legend(title = NULL,
nrow = 2)) +
theme(plot.margin = unit(c(ggarrange_margin_left,
ggarrange_margin_right,
ggarrange_margin_bottom,
ggarrange_margin_left),
"cm")) +
geom_rect(xmin = grey_background_xmin,
xmax = grey_background_xmax,
ymin = grey_background_ymin,
ymax = grey_background_ymax,
fill = grey_background_fill,
alpha = grey_background_alpha,
color = grey_background_color)
}# Combine plots of alpha, beta, gamma biodiversity and biomass.
p_combined = ggarrange(plot.single.plot("mean_shannon") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text", size = paper_labels_size) +
font("ylab", size = paper_labels_size),
plot.single.plot("bray_curtis") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size),
plot.single.plot("metaecosystem_richness") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size),
plot.single.plot("total_metaecosystem_bioarea_mm2") +
font("legend.text",
size = paper_labels_size) +
font("xlab",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size) +
scale_x_continuous(breaks = unique(data_for_plotting$day)),
heights = c(0.8, 0.8, 0.8, 1),
nrow = 4,
common.legend = TRUE,
align = "v",
labels = c("(a)", "(b)", "(c)", "(d)"),
label.x = 0.1,
label.y = 0.8) %>%
print()
# --- PREPARE DATA FOR PLOTTING --- #
# Define parameters
ecosystem_type_selected = c("Small connected to large",
"Small connected to small",
"Small unconnected",
"Large connected to small",
"Large connected to large",
"Large unconnected")
# Filter data
data_for_plotting = ds_ecosystems %>%
filter(ecosystem_type %in% ecosystem_type_selected,
disturbance == disturbance_selected)# --- CONSTRUCT FUNCTION TO PLOT BIOMASS/SHANNON OF SMALL AND LARGE ECOSYSTEMS --- #
plot.single.plot = function(response_variable_selected){
data_for_plotting %>%
filter(!is.na(!!sym(response_variable_selected))) %>%
summarySE(measurevar = response_variable_selected,
groupvars = c("day", "time_point", "ecosystem_type", "ecosystem_size", "connection")) %>%
ggplot(aes(x = day,
y = get(response_variable_selected),
group = interaction(day, ecosystem_type),
color = ecosystem_type,
linetype = ecosystem_type)) +
geom_point(stat = "summary",
fun = "mean",
position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(stat = "summary",
fun = "mean",
aes(group = ecosystem_type),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
geom_errorbar(aes(ymax = get(response_variable_selected) + ci,
ymin = get(response_variable_selected) - ci),
width = width_errorbar,
position = position_dodge(dodging)) +
labs(x = axis_names$axis_name[axis_names$variable == "day"],
y = axis_names$axis_name[axis_names$variable == response_variable_selected],
color = "") +
scale_color_manual(values = c("#993404",
"#993404",
"#993404",
"#3182bd",
"#3182bd",
"#3182bd"),
label = expression(S[L],
S[S],
S,
L[S],
L[L],
L)) +
scale_linetype_manual(values = c("solid",
"dashed",
"dotted",
"solid",
"dashed",
"dotted"),
label = expression(S[L],
S[S],
S,
L[S],
L[L],
L)) +
geom_vline(xintercept = resource_flow_days,
linetype = resource_flow_line_type,
color = resource_flow_line_colour,
linewidth = resource_flow_line_width) +
geom_hline(yintercept = 0,
color = zero_line_colour,
linetype = zero_line_line_type,
linewidth = zero_line_line_width) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm")) +
guides(color = guide_legend(title = NULL,
nrow = 3),
linetype = guide_legend(title = NULL,
nrow = 3)) +
geom_rect(xmin = grey_background_xmin,
xmax = grey_background_xmax,
ymin = grey_background_ymin,
ymax = grey_background_ymax,
fill = grey_background_fill,
alpha = grey_background_alpha,
color = grey_background_color)
}# --- COMBINE PLOTS OF SHANNON AND BIOMASS --- #
p_combined = ggarrange(plot.single.plot("shannon") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size),
plot.single.plot("bioarea_mm2_per_ml") +
font("legend.text",
size = paper_labels_size) +
font("xlab",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size) +
scale_x_continuous(breaks = unique(data_for_plotting$day)),
heights = c(0.8, 0.8, 1),
nrow = 2,
align = "v",
labels = c("(a)", "(b)"),
label.x = 0.1,
label.y = 0.8,
common.legend = TRUE) %>%
print()## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced
# --- PREPARE DATA FOR PLOTTING --- #
# Define parameters
ecosystem_type_selected = c("Medium connected to medium",
"Medium unconnected")
# Filter data
data_for_plotting = ds_ecosystems %>%
filter(ecosystem_type %in% ecosystem_type_selected,
disturbance == disturbance_selected,
!is.na(!!sym(response_variable_selected)))# --- CONSTRUCT FUNCTION TO PLOT BIOMASS/SHANNON OF MEDIUM ECOSYSTEMS --- #
plot.single.plot = function(response_variable_selected){
data_for_plotting %>%
summarySE(measurevar = response_variable_selected,
groupvars = c("day", "time_point", "ecosystem_type", "ecosystem_size", "connection")) %>%
ggplot(aes(x = day,
y = get(response_variable_selected),
group = interaction(day, ecosystem_type),
color = ecosystem_type,
linetype = ecosystem_type)) +
geom_point(stat = "summary",
fun = "mean",
position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(stat = "summary",
fun = "mean",
aes(group = ecosystem_type),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
geom_errorbar(aes(ymax = get(response_variable_selected) + ci,
ymin = get(response_variable_selected) - ci),
width = width_errorbar,
position = position_dodge(dodging)) +
labs(x = axis_names$axis_name[axis_names$variable == "day"],
y = axis_names$axis_name[axis_names$variable == response_variable_selected],
color = "") +
scale_color_manual(values = c("#d95f0e",
"#d95f0e"),
label = expression(M[M],
M)) +
scale_linetype_manual(values = c("dashed",
"dotted"),
label = expression(M[M],
M)) +
geom_vline(xintercept = resource_flow_days,
linetype = resource_flow_line_type,
color = resource_flow_line_colour,
linewidth = resource_flow_line_width) +
geom_hline(yintercept = 0,
color = zero_line_colour,
linetype = zero_line_line_type,
linewidth = zero_line_line_width) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm")) +
guides(color = guide_legend(title = NULL,
nrow = 3),
linetype = guide_legend(title = NULL,
nrow = 3)) +
geom_rect(xmin = grey_background_xmin,
xmax = grey_background_xmax,
ymin = grey_background_ymin,
ymax = grey_background_ymax,
fill = grey_background_fill,
alpha = grey_background_alpha,
color = grey_background_color)
}# --- COMBINE PLOTS OF SHANNON AND BIOMASS --- #
p_combined = ggarrange(plot.single.plot("shannon") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size),
plot.single.plot("bioarea_mm2_per_ml") +
font("legend.text",
size = paper_labels_size) +
font("xlab",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size) +
scale_x_continuous(breaks = unique(data_for_plotting$day)),
heights = c(0.8, 0.8, 1),
nrow = 2,
align = "v",
labels = c("(a)", "(b)"),
label.x = 0.1,
label.y = 0.8,
common.legend = TRUE) %>%
print()
# --- DEFINE RESPONSE VARIABLE YOU WANT TO PLOT --- #
response_variable = "auto_hetero_ratio"# --- PREPARE DATA FOR PLOTTING --- #
# Define parameters
ecosystem_type_selected = c("S",
"M",
"L")
# Manipulate data
data_for_plotting = ds_ecosystems %>%
mutate(ecosystem_type = case_when(ecosystem_type == "Small unconnected" ~ "S",
ecosystem_type == "Medium unconnected" ~ "M",
ecosystem_type == "Large unconnected" ~ "L")) %>%
filter(ecosystem_type %in% ecosystem_type_selected,
disturbance == disturbance_selected,
!is.na(!!sym(response_variable)))# --- CONSTRUCT PLOT --- #
p = data_for_plotting %>%
# Manipulate
summarySE(measurevar = response_variable,
groupvars = c("day", "ecosystem_type", "ecosystem_size", "connection")) %>%
# Create plot
ggplot(aes(x = day,
y = get(response_variable),
group = interaction(day, ecosystem_type),
color = ecosystem_type)) +
# Points
geom_point(stat = "summary",
fun = "mean",
position = position_dodge(dodging),
size = treatment_points_size) +
geom_errorbar(aes(ymax = get(response_variable) + ci,
ymin = get(response_variable) - ci),
width = width_errorbar,
position = position_dodge(dodging)) +
# Lines
geom_line(stat = "summary",
fun = "mean",
aes(group = ecosystem_type),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
# Axes and legend
labs(x = axis_names$axis_name[axis_names$variable == "day"],
y = axis_names$axis_name[axis_names$variable == response_variable],
color = "") +
scale_x_continuous(breaks = unique(data_for_plotting$day)) +
guides(color = guide_legend(title = NULL,
nrow = 1),
linetype = guide_legend(title = NULL,
nrow = 1)) +
scale_color_manual(values = c("#000000",
"#737373",
"#bdbdbd")) +
# Extra graphic elements
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm"),
axis.title.x = element_text(size = paper_labels_size),
axis.title.y = element_text(size = paper_labels_size),
legend.text = element_text(size = paper_labels_size)) +
geom_rect(xmin = grey_background_xmin,
xmax = grey_background_xmax,
ymin = grey_background_ymin,
ymax = grey_background_ymax,
fill = grey_background_fill,
alpha = grey_background_alpha,
color = grey_background_color) +
geom_hline(yintercept = 0,
color = zero_line_colour,
linetype = zero_line_line_type,
linewidth = zero_line_line_width) +
geom_vline(xintercept = resource_flow_days,
linetype = resource_flow_line_type,
color = resource_flow_line_colour,
linewidth = resource_flow_line_width)## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedp
Figures to present
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.# --- DEFINE PLOTTING PARAMETERS --- #
legend_row_n_input = 2
size_y_axis = 22
x_min = -0.2
x_max = 30
y_min = -0.8
y_max = 3# --- PREPARE DATA FOR PLOTTING --- #
# Define parameters
ecosystem_type_all = c("Small connected to large",
"Small unconnected",
"Small connected to small",
"Large connected to small",
"Large unconnected",
"Large connected to large")
response_variable_selected = "shannon"
# Filter data
data_for_plotting = ds_ecosystems %>%
filter(ecosystem_type %in% ecosystem_type_all,
disturbance == disturbance_selected)# --- CONSTRUCT PLOTS --- #
# Define parameters
plots = NULL
# Construct plots
plots[[1]] = plot.ecosystems.empty.presentations(y_min_alpha,
y_max_alpha,
"shannon")
for (i in 1:length(ecosystem_type_all)+1) {
plots[[i+1]] = plot.ecosystems.points.presentations(data = data_for_plotting,
ecosystem_type_selected = ecosystem_type_all[1:i],
response_variable_selected) +
theme(plot.margin = unit(c(ggarrange_margin_left,
ggarrange_margin_right,
ggarrange_margin_bottom,
ggarrange_margin_left),
"cm"),
legend.position = "none") +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01)) +
xlim(x_min, x_max) +
ylim(y_min, y_max)
}## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced
## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced
## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced
## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced
## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced
## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced# --- COMBINE PLOTS --- #
# Define parameters
p_combined = NULL
p_combined[[1]] = plots[[1]]
# Combine plots
for(i in 1:length(ecosystem_type_all)+1){
p_combined[[i]] = ggarrange(plots[[i]] +
theme(axis.text = element_text(size = presentation_axes_size)) +
font("legend.text", size = size_legend) +
font("xlab", size = size_y_axis) +
font("ylab", size = size_y_axis),
nrow = 1,
align = "v",
label.x = 0.1,
label.y = 0.8)
}## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).# --- SAVE COMBINED PLOTS --- #
for(i in 1:length(ecosystem_type_all)+1){
png(file = here("3_results",
"figures",
disturbance_selected,
"presentations",
paste0("ecosystems_connected_diversity_", i, ".png")),
width = presentation_figure_width,
height = presentation_figure_height,
units = presentation_figure_units,
res = presentation_figure_res)
print(p_combined[[i]])
dev.off()
}Low disturbance
disturbance_selected = "low"# --- PREPARE DATA FOR PLOTTING --- #
# Define what you want to plot
metaecosystem_type_selected = c("Medium-Medium",
"Small-Large")
# Filter dataset
data_for_plotting = ds_metaecosystems %>%
filter(metaecosystem_type %in% metaecosystem_type_selected,
disturbance == disturbance_selected)# Write function to plot a response variable. Afterwards you can use this function to plot alpha, beta, gamma diversity, and biomass.
plot.single.plot = function(response_variable_selected){
data_for_plotting %>%
filter(metaecosystem_type %in% metaecosystem_type_selected,
!is.na(!!sym(response_variable_selected))) %>%
summarySE(measurevar = response_variable_selected,
groupvars = c("day", "ecosystem_size_symmetry", "connection")) %>%
ggplot(aes(x = day,
y = get(response_variable_selected),
group = interaction(day, ecosystem_size_symmetry, connection),
color = ecosystem_size_symmetry,
linetype = connection)) +
geom_point(stat = "summary",
fun = "mean",
position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(stat = "summary",
fun = "mean",
aes(group = interaction(ecosystem_size_symmetry, connection)),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
geom_errorbar(aes(ymax = get(response_variable_selected) + ci,
ymin = get(response_variable_selected) - ci),
width = width_errorbar,
position = position_dodge(dodging)) +
labs(x = axis_names$axis_name[axis_names$variable == "day"],
y = axis_names$axis_name[axis_names$variable == response_variable_selected],
color = "") +
scale_color_manual(values = treatment_colours_paper) +
scale_linetype_manual(values = treatment_linetype_paper) +
geom_vline(xintercept = resource_flow_days,
linetype = resource_flow_line_type,
color = resource_flow_line_colour,
linewidth = resource_flow_line_width) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm")) +
guides(color = guide_legend(title = NULL,
nrow = 2),
linetype = guide_legend(title = NULL,
nrow = 2)) +
theme(plot.margin = unit(c(ggarrange_margin_left,
ggarrange_margin_right,
ggarrange_margin_bottom,
ggarrange_margin_left),
"cm")) +
geom_rect(xmin = grey_background_xmin,
xmax = grey_background_xmax,
ymin = grey_background_ymin,
ymax = grey_background_ymax,
fill = grey_background_fill,
alpha = grey_background_alpha,
color = grey_background_color)
}# Combine plots of alpha, beta, gamma biodiversity and biomass.
p_combined = ggarrange(plot.single.plot("mean_shannon") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text", size = paper_labels_size) +
font("ylab", size = paper_labels_size),
plot.single.plot("bray_curtis") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size),
plot.single.plot("metaecosystem_richness") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size),
plot.single.plot("total_metaecosystem_bioarea_mm2") +
font("legend.text",
size = paper_labels_size) +
font("xlab",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size) +
scale_x_continuous(breaks = unique(data_for_plotting$day)),
heights = c(0.8, 0.8, 0.8, 1),
nrow = 4,
common.legend = TRUE,
align = "v",
labels = c("(a)", "(b)", "(c)", "(d)"),
label.x = 0.1,
label.y = 0.8) %>%
print()
# --- PREPARE DATA FOR PLOTTING --- #
# Define parameters
ecosystem_type_selected = c("Small connected to large",
"Small connected to small",
"Small unconnected",
"Large connected to small",
"Large connected to large",
"Large unconnected")
# Filter data
data_for_plotting = ds_ecosystems %>%
filter(ecosystem_type %in% ecosystem_type_selected,
disturbance == disturbance_selected)# --- CONSTRUCT FUNCTION TO PLOT BIOMASS/SHANNON OF SMALL AND LARGE ECOSYSTEMS --- #
plot.single.plot = function(response_variable_selected){
data_for_plotting %>%
filter(!is.na(!!sym(response_variable_selected))) %>%
summarySE(measurevar = response_variable_selected,
groupvars = c("day", "time_point", "ecosystem_type", "ecosystem_size", "connection")) %>%
ggplot(aes(x = day,
y = get(response_variable_selected),
group = interaction(day, ecosystem_type),
color = ecosystem_type,
linetype = ecosystem_type)) +
geom_point(stat = "summary",
fun = "mean",
position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(stat = "summary",
fun = "mean",
aes(group = ecosystem_type),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
geom_errorbar(aes(ymax = get(response_variable_selected) + ci,
ymin = get(response_variable_selected) - ci),
width = width_errorbar,
position = position_dodge(dodging)) +
labs(x = axis_names$axis_name[axis_names$variable == "day"],
y = axis_names$axis_name[axis_names$variable == response_variable_selected],
color = "") +
scale_color_manual(values = c("#993404",
"#993404",
"#993404",
"#3182bd",
"#3182bd",
"#3182bd"),
label = expression(S[L],
S[S],
S,
L[S],
L[L],
L)) +
scale_linetype_manual(values = c("solid",
"dashed",
"dotted",
"solid",
"dashed",
"dotted"),
label = expression(S[L],
S[S],
S,
L[S],
L[L],
L)) +
geom_vline(xintercept = resource_flow_days,
linetype = resource_flow_line_type,
color = resource_flow_line_colour,
linewidth = resource_flow_line_width) +
geom_hline(yintercept = 0,
color = zero_line_colour,
linetype = zero_line_line_type,
linewidth = zero_line_line_width) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm")) +
guides(color = guide_legend(title = NULL,
nrow = 3),
linetype = guide_legend(title = NULL,
nrow = 3)) +
geom_rect(xmin = grey_background_xmin,
xmax = grey_background_xmax,
ymin = grey_background_ymin,
ymax = grey_background_ymax,
fill = grey_background_fill,
alpha = grey_background_alpha,
color = grey_background_color)
}# --- COMBINE PLOTS OF SHANNON AND BIOMASS --- #
p_combined = ggarrange(plot.single.plot("shannon") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size),
plot.single.plot("bioarea_mm2_per_ml") +
font("legend.text",
size = paper_labels_size) +
font("xlab",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size) +
scale_x_continuous(breaks = unique(data_for_plotting$day)),
heights = c(0.8, 0.8, 1),
nrow = 2,
align = "v",
labels = c("(a)", "(b)"),
label.x = 0.1,
label.y = 0.8,
common.legend = TRUE) %>%
print()
# --- PREPARE DATA FOR PLOTTING --- #
# Define parameters
ecosystem_type_selected = c("Medium connected to medium",
"Medium unconnected")
# Filter data
data_for_plotting = ds_ecosystems %>%
filter(ecosystem_type %in% ecosystem_type_selected,
disturbance == disturbance_selected,
!is.na(!!sym(response_variable_selected)))# --- CONSTRUCT FUNCTION TO PLOT BIOMASS/SHANNON OF MEDIUM ECOSYSTEMS --- #
plot.single.plot = function(response_variable_selected){
data_for_plotting %>%
summarySE(measurevar = response_variable_selected,
groupvars = c("day", "time_point", "ecosystem_type", "ecosystem_size", "connection")) %>%
ggplot(aes(x = day,
y = get(response_variable_selected),
group = interaction(day, ecosystem_type),
color = ecosystem_type,
linetype = ecosystem_type)) +
geom_point(stat = "summary",
fun = "mean",
position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(stat = "summary",
fun = "mean",
aes(group = ecosystem_type),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
geom_errorbar(aes(ymax = get(response_variable_selected) + ci,
ymin = get(response_variable_selected) - ci),
width = width_errorbar,
position = position_dodge(dodging)) +
labs(x = axis_names$axis_name[axis_names$variable == "day"],
y = axis_names$axis_name[axis_names$variable == response_variable_selected],
color = "") +
scale_color_manual(values = c("#d95f0e",
"#d95f0e"),
label = expression(M[M],
M)) +
scale_linetype_manual(values = c("dashed",
"dotted"),
label = expression(M[M],
M)) +
geom_vline(xintercept = resource_flow_days,
linetype = resource_flow_line_type,
color = resource_flow_line_colour,
linewidth = resource_flow_line_width) +
geom_hline(yintercept = 0,
color = zero_line_colour,
linetype = zero_line_line_type,
linewidth = zero_line_line_width) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm")) +
guides(color = guide_legend(title = NULL,
nrow = 3),
linetype = guide_legend(title = NULL,
nrow = 3)) +
geom_rect(xmin = grey_background_xmin,
xmax = grey_background_xmax,
ymin = grey_background_ymin,
ymax = grey_background_ymax,
fill = grey_background_fill,
alpha = grey_background_alpha,
color = grey_background_color)
}# --- COMBINE PLOTS OF SHANNON AND BIOMASS --- #
p_combined = ggarrange(plot.single.plot("shannon") +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size),
plot.single.plot("bioarea_mm2_per_ml") +
font("legend.text",
size = paper_labels_size) +
font("xlab",
size = paper_labels_size) +
font("ylab",
size = paper_labels_size) +
scale_x_continuous(breaks = unique(data_for_plotting$day)),
heights = c(0.8, 0.8, 1),
nrow = 2,
align = "v",
labels = c("(a)", "(b)"),
label.x = 0.1,
label.y = 0.8,
common.legend = TRUE) %>%
print()
# --- DEFINE RESPONSE VARIABLE YOU WANT TO PLOT --- #
response_variable = "auto_hetero_ratio"# --- PREPARE DATA FOR PLOTTING --- #
# Define parameters
ecosystem_type_selected = c("S",
"M",
"L")
# Manipulate data
data_for_plotting = ds_ecosystems %>%
mutate(ecosystem_type = case_when(ecosystem_type == "Small unconnected" ~ "S",
ecosystem_type == "Medium unconnected" ~ "M",
ecosystem_type == "Large unconnected" ~ "L")) %>%
filter(ecosystem_type %in% ecosystem_type_selected,
disturbance == disturbance_selected,
!is.na(!!sym(response_variable)))# --- CONSTRUCT PLOT --- #
p = data_for_plotting %>%
# Manipulate
summarySE(measurevar = response_variable,
groupvars = c("day", "ecosystem_type", "ecosystem_size", "connection")) %>%
# Create plot
ggplot(aes(x = day,
y = get(response_variable),
group = interaction(day, ecosystem_type),
color = ecosystem_type)) +
# Points
geom_point(stat = "summary",
fun = "mean",
position = position_dodge(dodging),
size = treatment_points_size) +
geom_errorbar(aes(ymax = get(response_variable) + ci,
ymin = get(response_variable) - ci),
width = width_errorbar,
position = position_dodge(dodging)) +
# Lines
geom_line(stat = "summary",
fun = "mean",
aes(group = ecosystem_type),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
# Axes and legend
labs(x = axis_names$axis_name[axis_names$variable == "day"],
y = axis_names$axis_name[axis_names$variable == response_variable],
color = "") +
scale_x_continuous(breaks = unique(data_for_plotting$day)) +
guides(color = guide_legend(title = NULL,
nrow = 1),
linetype = guide_legend(title = NULL,
nrow = 1)) +
scale_color_manual(values = c("#000000",
"#737373",
"#bdbdbd")) +
# Extra graphic elements
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = legend_position,
legend.key.width = unit(legend_width_cm, "cm"),
axis.title.x = element_text(size = paper_labels_size),
axis.title.y = element_text(size = paper_labels_size),
legend.text = element_text(size = paper_labels_size)) +
geom_rect(xmin = grey_background_xmin,
xmax = grey_background_xmax,
ymin = grey_background_ymin,
ymax = grey_background_ymax,
fill = grey_background_fill,
alpha = grey_background_alpha,
color = grey_background_color) +
geom_hline(yintercept = 0,
color = zero_line_colour,
linetype = zero_line_line_type,
linewidth = zero_line_line_width) +
geom_vline(xintercept = resource_flow_days,
linetype = resource_flow_line_type,
color = resource_flow_line_colour,
linewidth = resource_flow_line_width)
p
Figures to present
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.# --- DEFINE PLOTTING PARAMETERS --- #
legend_row_n_input = 2
size_y_axis = 22
x_min = -0.2
x_max = 30
y_min = -0.8
y_max = 3# --- PREPARE DATA FOR PLOTTING --- #
# Define parameters
ecosystem_type_all = c("Small connected to large",
"Small unconnected",
"Small connected to small",
"Large connected to small",
"Large unconnected",
"Large connected to large")
response_variable_selected = "shannon"
# Filter data
data_for_plotting = ds_ecosystems %>%
filter(ecosystem_type %in% ecosystem_type_all,
disturbance == disturbance_selected)# --- CONSTRUCT PLOTS --- #
# Define parameters
plots = NULL
# Construct plots
plots[[1]] = plot.ecosystems.empty.presentations(y_min_alpha,
y_max_alpha,
"shannon")
for (i in 1:length(ecosystem_type_all)+1) {
plots[[i+1]] = plot.ecosystems.points.presentations(data = data_for_plotting,
ecosystem_type_selected = ecosystem_type_all[1:i],
response_variable_selected) +
theme(plot.margin = unit(c(ggarrange_margin_left,
ggarrange_margin_right,
ggarrange_margin_bottom,
ggarrange_margin_left),
"cm"),
legend.position = "none") +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01)) +
xlim(x_min, x_max) +
ylim(y_min, y_max)
}# --- COMBINE PLOTS --- #
# Define parameters
p_combined = NULL
p_combined[[1]] = plots[[1]]
# Combine plots
for(i in 1:length(ecosystem_type_all)+1){
p_combined[[i]] = ggarrange(plots[[i]] +
theme(axis.text = element_text(size = presentation_axes_size)) +
font("legend.text", size = size_legend) +
font("xlab", size = size_y_axis) +
font("ylab", size = size_y_axis),
nrow = 1,
align = "v",
label.x = 0.1,
label.y = 0.8)
}## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.
## No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's linetype values.## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).# --- SAVE COMBINED PLOTS --- #
for(i in 1:length(ecosystem_type_all)+1){
png(file = here("3_results",
"figures",
disturbance_selected,
"presentations",
paste0("ecosystems_connected_diversity_", i, ".png")),
width = presentation_figure_width,
height = presentation_figure_height,
units = presentation_figure_units,
res = presentation_figure_res)
print(p_combined[[i]])
dev.off()
}Analysis scripts
Video analysis
To analyse the videos I took of the ecosystems, I used the package BEMOVI. For this, I had to use the powerful computer. Below is the code utilised for video analysis on the powerful computer.
# Clear workspace
rm(list = ls())
# Set working directory
setwd("/media/mendel-himself/ID_061_Ema2/PatchSizePilot/training")
# Load required libraries
# library(devtools)
# install_github("femoerman/bemovi", ref="master")
library(bemovi)
library(parallel)
library(doParallel)
library(foreach)
# Define memory allocation parameters (in MB)
memory.alloc <- 240000 # Total memory allocated
memory.per.identifier <- 40000 # Memory per identifier
memory.per.linker <- 5000 # Memory per linker
memory.per.overlay <- 60000 # Memory per overlay
# Set paths for tools and particle linker
tools.path <- "/home/mendel-himself/bemovi_tools/" # Path to tools folder
to.particlelinker <- tools.path
# Set directories and file names
to.data <- paste(getwd(), "/", sep = "")
video.description.folder <- "0_video_description/"
video.description.file <- "video_description.txt"
raw.video.folder <- "1_raw/"
raw.avi.folder <- "1a_raw_avi/"
metadata.folder <- "1b_raw_meta/"
particle.data.folder <- "2_particle_data/"
trajectory.data.folder <- "3_trajectory_data/"
temp.overlay.folder <- "4a_temp_overlays/"
overlay.folder <- "4_overlays/"
merged.data.folder <- "5_merged_data/"
ijmacs.folder <- "ijmacs/"
######################################################################
# VIDEO PARAMETERS
# Define video parameters
fps <- 25 # Video frame rate (frames per second)
total_frames <- 125 # Total length of video (frames)
width <- 2048 # Video width (pixels)
height <- 2048 # Video height (pixels)
measured_volume <- 34.4 # Measured volume (microliters) for Leica M205 C with 1.6 fold magnification, sample height 0.5 mm and Hamamatsu Orca Flash 4
pixel_to_scale <- 4.05 # Size of a pixel (micrometers) for Leica M205 C with 1.6 fold magnification, sample height 0.5 mm and Hamamatsu Orca Flash 4
video.format <- "cxd" # Video file format (avi, cxd, mov, tiff)
difference.lag <- 10 # Difference lag
thresholds <- c(13, 255) # Threshold values of pixel intensity (considered a measure of pixel "whiteness") for determining if a pixel belongs to an individual rather than the background
######################################################################
# FILTERING PARAMETERS
# optimized for Perfex Pro 10 stereomicrocope with Perfex SC38800 (IDS UI-3880LE-M-GL) camera
# tested stereomicroscopes: Perfex Pro 10, Nikon SMZ1500, Leica M205 C
# tested cameras: Perfex SC38800, Canon 5D Mark III, Hamamatsu Orca Flash 4
# tested species: Tet, Col, Pau, Pca, Eug, Chi, Ble, Ceph, Lox, Spi
particle_min_size <- 10 # Minimum particle size (pixels)
particle_max_size <- 1000 # Maximum particle size (pixels)
trajectory_link_range <- 3 # Number of adjacent frames for linking particles
trajectory_displacement <- 16 # Maximum displacement of a particle between frames
# Filtering criteria
filter_min_net_disp <- 25 # Minimum net displacement (µm)
filter_min_duration <- 1 # Minimum duration (s)
filter_detection_freq <- 0.1 # Minimum detection frequency (1/s)
filter_median_step_length <- 3 # Minimum median step length (µm)
######################################################################
# VIDEO ANALYSIS
# Check if all tools are installed and set permissions
check_tools_folder(tools.path)
system(paste0("chmod a+x ", tools.path, "bftools/bf.sh"))
system(paste0("chmod a+x ", tools.path, "bftools/bfconvert"))
system(paste0("chmod a+x ", tools.path, "bftools/showinf"))
# Convert video files to compressed avi format
convert_to_avi(to.data,
raw.video.folder,
raw.avi.folder,
metadata.folder,
tools.path,
fps,
video.format)
# Uncomment the following lines for testing
# check_video_file_names(to.data, raw.avi.folder, video.description.folder, video.description.file)
# check_threshold_values(to.data, raw.avi.folder, ijmacs.folder, 2, difference.lag, thresholds, tools.path, memory.alloc)
# Identify particles in the video
locate_and_measure_particles(to.data,
raw.avi.folder,
particle.data.folder,
difference.lag,
min_size = particle_min_size,
max_size = particle_max_size,
thresholds = thresholds,
tools.path,
memory = memory.alloc,
memory.per.identifier = memory.per.identifier,
max.cores = detectCores() - 1)
# Link particles across frames to form trajectories
link_particles(to.data,
particle.data.folder,
trajectory.data.folder,
linkrange = trajectory_link_range,
disp = trajectory_displacement,
start_vid = 1,
memory = memory.alloc,
memory_per_linkerProcess = memory.per.linker,
raw.avi.folder,
max.cores = detectCores() - 1,
max_time = 1)
# Merge video description file with particle data
merge_data(to.data,
particle.data.folder,
trajectory.data.folder,
video.description.folder,
video.description.file,
merged.data.folder)
# Load the merged data
load(paste0(to.data, merged.data.folder, "Master.RData"))
# Filter trajectory data based on defined criteria
trajectory.data.filtered <- filter_data(trajectory.data,
filter_min_net_disp,
filter_min_duration,
filter_detection_freq,
filter_median_step_length)
# Summarize trajectory data to individual-based data
morph_mvt <- summarize_trajectories(trajectory.data.filtered,
calculate.median = F,
write = T,
to.data,
merged.data.folder)
# Summarize sample level data
summarize_populations(trajectory.data.filtered,
morph_mvt,
write = T,
to.data,
merged.data.folder,
video.description.folder,
video.description.file,
total_frames)
# Create overlays for validation
create.subtitle.overlays(to.data,
traj.data = trajectory.data.filtered,
raw.video.folder,
raw.avi.folder,
temp.overlay.folder,
overlay.folder,
fps,
vid.length = total_frames / fps,
width,
height,
tools.path = tools.path,
overlay.type = "number",
video.format)
# Create overlays (old method)
create_overlays(traj.data = trajectory.data.filtered,
to.data = to.data,
merged.data.folder = merged.data.folder,
raw.video.folder = raw.avi.folder,
temp.overlay.folder = "4a_temp_overlays_old/",
overlay.folder = "4_overlays_old/",
width = width,
height = height,
difference.lag = difference.lag,
type = "traj",
predict_spec = F,
contrast.enhancement = 1,
IJ.path = "/home/mendel-himself/bemovi_tools",
memory = memory.alloc,
max.cores = detectCores() - 1,
memory.per.overlay = memory.per.overlay)Species identification
To avoid transferring all the data from the powerful computer, I performed species identification on that system and subsequently imported the results into the Rstudio folder on my personal computer. Below is the code utilised for species identification on the powerful computer.
# Clear the workspace
rm(list = ls())
# Uncomment and install required packages if not already installed
#install.packages("e1071",dependencies = T)
#install.packages("devtools",dependencies = T)
#install_github("pennekampster/bemovi", ref="master")
#library(devtools)
# Load required libraries
library(bemovi)
library(e1071)
library("here")
library("tidyverse")
# Define time points in the experiment
time_points_in_experiment = c("t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7")
# Loop through each time point in the experiment
for (time_point in time_points_in_experiment) {
# Define folder names and paths
video.description.folder = "0_video_description/"
video.description.file = "video_description.txt"
merged.data.folder = "5_merged_data/"
monocultures_folder_path = here("biomass_analysis", "training", "")
mixed_cultures_folder_path = here("biomass_analysis", time_point, "")
#Parameters used in the video analysis script
fps = 25
nsv = 5
measured_volume = 34.4
pixel_to_scale = 4.05
filter_min_net_disp = 25
filter_min_duration = 1
filter_detection_freq = 0.1
filter_median_step_length = 3
# Load master dataset of mono-cultures
load(paste0(monocultures_folder_path, merged.data.folder, "Master.RData"))
trajectory.data_monocultures = trajectory.data
rm(trajectory.data)
# Filter the master data of mono-cultures using the same parameters as in the video analysis script
trajectory.data_monocultures.filtered = filter_data(trajectory.data_monocultures,
filter_min_net_disp,
filter_min_duration,
filter_detection_freq,
filter_median_step_length)
# Summarize trajectory data to individual-based data
morph_mvt = summarize_trajectories(data = trajectory.data_monocultures.filtered,
calculate.median = FALSE,
write = TRUE,
to.data = monocultures_folder_path,
merged.data.folder = merged.data.folder) %>%
mutate(comment = NULL)
# Prepare training data by removing incomplete cases
training_data = morph_mvt[complete.cases(morph_mvt), ]
# Train SVM model on the training data
svm1 = svm(
factor(species) ~
mean_grey +
sd_grey +
mean_area +
sd_area +
mean_perimeter +
mean_turning +
sd_turning +
sd_perimeter +
mean_major +
sd_major +
mean_minor +
sd_minor +
mean_ar +
sd_ar +
duration +
max_net +
net_disp +
net_speed +
gross_disp +
max_step +
min_step +
sd_step +
sd_gross_speed +
max_gross_speed +
min_gross_speed ,
data = training_data,
probability = T,
na.action = na.pass)
# Generate and print confusion matrix
confusion.matrix = table(svm1$fitted, training_data$species)
confusion.matrix.nd = confusion.matrix
diag(confusion.matrix.nd) = 0
svm1$confusion = cbind(confusion.matrix,
class.error = rowSums(confusion.matrix.nd) / rowSums(confusion.matrix))
print(paste("Confusion matrix of time point", time_point))
print(svm1$confusion)
# Extract unique species names
species.names = unique(trajectory.data_monocultures$species)
# Load mixed cultures dataset
load(paste0(mixed_cultures_folder_path, merged.data.folder, "Master.RData"))
trajectory.data_mixed = trajectory.data
rm(trajectory.data)
# Filter mixed cultures data using the same parameters
trajectory.data_mixed.filtered = filter_data(trajectory.data_mixed,
filter_min_net_disp,
filter_min_duration,
filter_detection_freq,
filter_median_step_length)
# Summarize trajectory data to individual-based data
morph_mvt = summarize_trajectories(data = trajectory.data_mixed.filtered,
calculate.median = FALSE,
write = TRUE,
to.data = mixed_cultures_folder_path,
merged.data.folder = merged.data.folder)[, which(colnames(morph_mvt) != "Col_manual")] %>%
mutate(comment = NULL)
# Prepare data for prediction by removing incomplete cases
data.to.predict = morph_mvt[complete.cases(morph_mvt),]
# Predict species using the trained SVM model
p.id = predict(object = svm1, data.to.predict, type = "response")
data.to.predict$predicted_species = as.character(p.id)
# Summarize population data
pop.data = summarize_populations(traj.data = trajectory.data_monocultures.filtered,
sum.data = morph_mvt,
write = TRUE,
to.data = mixed_cultures_folder_path,
merged.data.folder = merged.data.folder,
video.description.folder = video.description.folder,
video.description.file = video.description.file,
total_frame = fps * nsv)
# Function to calculate species density
species.density = function(sample_output,
indiv_predicted,
species_names,
total_frames,
mv = measured_volume) {
samples = unique(indiv_predicted$file)
sp.dens = matrix(0,
nrow(sample_output),
length(species_names))
colnames(sp.dens) = species_names
for (i in 1:length(samples)) {
indiv = subset(indiv_predicted, file == samples[i])
spec = unique(indiv$predicted_species)
for (j in 1:length(spec)) {
all.indiv.sp = subset(indiv,
predicted_species == spec[j])
dens = sum(all.indiv.sp$N_frames) / total_frames / mv
sp.dens[which(sample_output$file == as.character(samples[i])), which(species_names == spec[j])] = dens
}
}
return(cbind(sample_output, sp.dens))
}
# Calculate species density for the current time point
output = species.density(pop.data,
data.to.predict,
species.names,
total_frames = fps * nsv,
mv = measured_volume)
# Save the species density results to a CSV file
file_name = paste0("species_ID_", time_point, ".csv")
write.csv(output, here("biomass_analysis", "species_ID_results", file_name))
rm(output)
}Other
Running time
## Time difference of 1.9 minsUseful code
If you want to change a certain part of the code using the following code in Unix:
#Rmd script
cd /Users/Ema/Documents/Github/PatchSize/3_r_files
sed -i '' 's/old_string/new_string/g' *.Rmd
#R script
cd /Users/ema/Documents/GitHub/PatchSize/3_r_files/functions
sed -i '' 's/old_string/new_string/g' *.Ryou want to share a dataset and get a reproducible object, use the following R code:
dput()Crashed cultures
The only type of ecosystem where all cultures crashed was small connected to small at high disturbance.
R version
R.version.string## [1] "R version 4.3.2 (2023-10-31)"R package versions
The R packages we used with their version are as follows:
sessionInfo()## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.2.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Zurich
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] conflicted_1.2.0 broom.mixed_0.2.9.5 emmeans_1.10.4
## [4] combinat_0.0-8 Rmisc_1.5.1 betapart_1.6
## [7] vegan_2.6-6.1 lattice_0.22-6 permute_0.9-7
## [10] glmmTMB_1.1.10 lmerTest_3.1-3 lme4_1.1-35.4
## [13] Matrix_1.6-5 DHARMa_0.4.7 GGally_2.2.1
## [16] gridExtra_2.3 plotly_4.10.4 ggpubr_0.6.0
## [19] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
## [22] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
## [25] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
## [28] tidyverse_2.0.0 plyr_1.8.9 renv_1.0.7.9000
## [31] testthat_3.2.1.1 here_1.0.1
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8
## [4] magrittr_2.0.3 estimability_1.5.1 farver_2.1.2
## [7] nloptr_2.1.1 rmarkdown_2.27 vctrs_0.6.5
## [10] memoise_2.0.1 minqa_1.2.7 rstatix_0.7.2
## [13] htmltools_0.5.8.1 itertools_0.1-3 broom_1.0.6
## [16] sass_0.4.9 parallelly_1.38.0 bslib_0.7.0
## [19] desc_1.4.3 htmlwidgets_1.6.4 cachem_1.1.0
## [22] TMB_1.9.15 mime_0.12 lifecycle_1.0.4
## [25] minpack.lm_1.2-4 iterators_1.0.14 pkgconfig_2.0.3
## [28] gap_1.6 R6_2.5.1 fastmap_1.2.0
## [31] shiny_1.9.1 rbibutils_2.2.16 future_1.34.0
## [34] magic_1.6-1 digest_0.6.36 numDeriv_2016.8-1.1
## [37] colorspace_2.1-0 furrr_0.3.1 rprojroot_2.0.4
## [40] pkgload_1.3.4 qgam_1.3.4 labeling_0.4.3
## [43] fansi_1.0.6 timechange_0.3.0 httr_1.4.7
## [46] abind_1.4-5 mgcv_1.9-0 compiler_4.3.2
## [49] doParallel_1.0.17 withr_3.0.0 backports_1.5.0
## [52] carData_3.0-5 ggstats_0.6.0 highr_0.11
## [55] ggsignif_0.6.4 MASS_7.3-60 tools_4.3.2
## [58] ape_5.8 httpuv_1.6.15 glue_1.7.0
## [61] rcdd_1.6 promises_1.3.0 nlme_3.1-163
## [64] grid_4.3.2 cluster_2.1.4 generics_0.1.3
## [67] snow_0.4-4 gtable_0.3.5 tzdb_0.4.0
## [70] data.table_1.15.4 hms_1.1.3 car_3.1-2
## [73] utf8_1.2.4 foreach_1.5.2 pillar_1.9.0
## [76] later_1.3.2 splines_4.3.2 tidyselect_1.2.1
## [79] knitr_1.47 reformulas_0.3.0 xfun_0.45
## [82] brio_1.1.5 stringi_1.8.4 lazyeval_0.2.2
## [85] yaml_2.3.8 boot_1.3-30 evaluate_0.24.0
## [88] codetools_0.2-20 cli_3.6.3 xtable_1.8-4
## [91] geometry_0.4.7 Rdpack_2.6.1 munsell_0.5.1
## [94] jquerylib_0.1.4 Rcpp_1.0.12 doSNOW_1.0.20
## [97] globals_0.16.3 coda_0.19-4.1 parallel_4.3.2
## [100] picante_1.8.2 gap.datasets_0.0.6 listenv_0.9.1
## [103] viridisLite_0.4.2 mvtnorm_1.3-1 scales_1.3.0
## [106] rlang_1.1.4 cowplot_1.1.3 fastmatch_1.1-4
## [109] waldo_0.5.2